id stringlengths 9 13 | submitter stringlengths 4 48 | authors stringlengths 4 9.62k | title stringlengths 4 343 | comments stringlengths 2 480 ⌀ | journal-ref stringlengths 9 309 ⌀ | doi stringlengths 12 138 ⌀ | report-no stringclasses 277 values | categories stringlengths 8 87 | license stringclasses 9 values | orig_abstract stringlengths 27 3.76k | versions listlengths 1 15 | update_date stringlengths 10 10 | authors_parsed listlengths 1 147 | abstract stringlengths 24 3.75k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
q-bio/0603026 | Adiel Loinger | Azi Lipshtat, Adiel Loinger, Nathalie Q. Balaban and Ofer Biham | Genetic Toggle Switch Without Cooperative Binding | 10 pages,4 figures | null | 10.1103/PhysRevLett.96.188101 | null | q-bio.MN | null | Genetic switch systems with mutual repression of two transcription factors
are studied using deterministic and stochastic methods. Numerous studies have
concluded that cooperative binding is a necessary condition for the emergence
of bistability in these systems. Here we show that for a range of biologically
relevant conditions, a suitable combination of network structure and stochastic
effects gives rise to bistability even without cooperative binding.
| [
{
"created": "Wed, 22 Mar 2006 14:46:16 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Lipshtat",
"Azi",
""
],
[
"Loinger",
"Adiel",
""
],
[
"Balaban",
"Nathalie Q.",
""
],
[
"Biham",
"Ofer",
""
]
] | Genetic switch systems with mutual repression of two transcription factors are studied using deterministic and stochastic methods. Numerous studies have concluded that cooperative binding is a necessary condition for the emergence of bistability in these systems. Here we show that for a range of biologically relevant conditions, a suitable combination of network structure and stochastic effects gives rise to bistability even without cooperative binding. |
2201.07075 | Mario Martinez-Saito | Mario Martinez-Saito | Discrete scaling and criticality in a chain of adaptive excitable
integrators | null | null | 10.1016/j.chaos.2022.112574 | null | q-bio.NC nlin.AO | http://creativecommons.org/licenses/by/4.0/ | We describe a chain of unidirectionally coupled adaptive excitable elements
slowly driven by a stochastic process from one end and open at the other end,
as a minimal toy model of unresolved irreducible uncertainty in a system
performing inference through a hierarchical model. Threshold potentials adapt
slowly to ensure sensitivity without being wasteful. Activity and energy are
released as intermittent avalanches of pulses with a discrete scaling
distribution largely independent of the exogenous input form. Subthreshold
activities and threshold potentials exhibit Lorentzian temporal spectra, with a
power-law range determined by position in the chain. Subthreshold bistability
closely resembles empirical measurements of intracellular membrane potential.
We suggest that critical cortical cascades emerge from a trade-off between
metabolic power consumption and performance requirements in a critical world,
and that the temporal scaling patterns of brain electrophysiological recordings
ensue from weighted linear combinations of subthreshold activities and pulses
from different hierarchy levels.
| [
{
"created": "Tue, 18 Jan 2022 15:59:23 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Jun 2022 18:37:08 GMT",
"version": "v10"
},
{
"created": "Wed, 19 Jan 2022 15:55:39 GMT",
"version": "v2"
},
{
"created": "Sun, 23 Jan 2022 13:59:47 GMT",
"version": "v3"
},
{
"... | 2022-09-14 | [
[
"Martinez-Saito",
"Mario",
""
]
] | We describe a chain of unidirectionally coupled adaptive excitable elements slowly driven by a stochastic process from one end and open at the other end, as a minimal toy model of unresolved irreducible uncertainty in a system performing inference through a hierarchical model. Threshold potentials adapt slowly to ensure sensitivity without being wasteful. Activity and energy are released as intermittent avalanches of pulses with a discrete scaling distribution largely independent of the exogenous input form. Subthreshold activities and threshold potentials exhibit Lorentzian temporal spectra, with a power-law range determined by position in the chain. Subthreshold bistability closely resembles empirical measurements of intracellular membrane potential. We suggest that critical cortical cascades emerge from a trade-off between metabolic power consumption and performance requirements in a critical world, and that the temporal scaling patterns of brain electrophysiological recordings ensue from weighted linear combinations of subthreshold activities and pulses from different hierarchy levels. |
1801.08108 | Matthias Keil | Matthias S. Keil, Elisenda Roca-Moreno, and Angel Rodriguez-Vazquez | A neural model of the locust visual system for detection of object
approaches with real-world scenes | Originally published in the Proceedings of the Fourth IASTED
International Conference on Visualization, Imaging, and Image Processing,
September 6-8, 2004, Marbella, Spain (see
http://www.actapress.com/Abstract.aspx?paperId=18773) | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the central nervous systems of animals like pigeons and locusts, neurons
were identified which signal objects approaching the animal on a direct
collision course. Unraveling the neural circuitry for collision avoidance, and
identifying the underlying computational principles, is promising for building
vision-based neuromorphic architectures, which in the near future could find
applications in cars or planes. At the present there is no published model
available for robust detection of approaching objects under real-world
conditions. Here we present a computational architecture for signalling
impending collisions, based on known anatomical data of the locust \emph{lobula
giant movement detector} (LGMD) neuron. Our model shows robust performance even
in adverse situations, such as with approaching low-contrast objects, or with
highly textured and moving backgrounds. We furthermore discuss which components
need to be added to our model to convert it into a full-fledged
real-world-environment collision detector. KEYWORDS: Locust, LGMD, collision
detection, lateral inhibition, diffusion, ON-OFF-pathways, neuronal dynamics,
computer vision, image processing
| [
{
"created": "Wed, 24 Jan 2018 18:13:12 GMT",
"version": "v1"
}
] | 2018-01-25 | [
[
"Keil",
"Matthias S.",
""
],
[
"Roca-Moreno",
"Elisenda",
""
],
[
"Rodriguez-Vazquez",
"Angel",
""
]
] | In the central nervous systems of animals like pigeons and locusts, neurons were identified which signal objects approaching the animal on a direct collision course. Unraveling the neural circuitry for collision avoidance, and identifying the underlying computational principles, is promising for building vision-based neuromorphic architectures, which in the near future could find applications in cars or planes. At the present there is no published model available for robust detection of approaching objects under real-world conditions. Here we present a computational architecture for signalling impending collisions, based on known anatomical data of the locust \emph{lobula giant movement detector} (LGMD) neuron. Our model shows robust performance even in adverse situations, such as with approaching low-contrast objects, or with highly textured and moving backgrounds. We furthermore discuss which components need to be added to our model to convert it into a full-fledged real-world-environment collision detector. KEYWORDS: Locust, LGMD, collision detection, lateral inhibition, diffusion, ON-OFF-pathways, neuronal dynamics, computer vision, image processing |
2311.02471 | Sam Subbey | Salah Alrabeei, Talal Rahman, Sam Subbey | Efficient Large-Scale Simulation of Fish Schooling Behavior Using
Voronoi Tessellations and Fuzzy Clustering | 11 pages | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/4.0/ | This paper introduces an efficient approach to reduce the computational cost
of simulating collective behaviors, such as fish schooling, using
Individual-Based Models (IBMs). The proposed technique employs adaptive and
dynamic load-balancing domain partitioning, which utilizes unsupervised
machine-learning models to cluster a large number of simulated individuals into
sub-schools based on their spatial-temporal locations. It also utilizes Voronoi
tessellations to construct non-overlapping simulation subdomains. This approach
minimizes agent-to-agent communication and balances the load both spatially and
temporally, ultimately resulting in reduced computational complexity.
Experimental simulations demonstrate that this partitioning approach
outperforms the standard regular grid-based domain decomposition, achieving a
reduction in computational cost while maintaining spatial and temporal load
balance. The approach presented in this paper has the potential to be applied
to other collective behavior simulations requiring large-scale simulations with
a substantial number of individuals.
| [
{
"created": "Sat, 4 Nov 2023 18:11:09 GMT",
"version": "v1"
}
] | 2023-11-07 | [
[
"Alrabeei",
"Salah",
""
],
[
"Rahman",
"Talal",
""
],
[
"Subbey",
"Sam",
""
]
] | This paper introduces an efficient approach to reduce the computational cost of simulating collective behaviors, such as fish schooling, using Individual-Based Models (IBMs). The proposed technique employs adaptive and dynamic load-balancing domain partitioning, which utilizes unsupervised machine-learning models to cluster a large number of simulated individuals into sub-schools based on their spatial-temporal locations. It also utilizes Voronoi tessellations to construct non-overlapping simulation subdomains. This approach minimizes agent-to-agent communication and balances the load both spatially and temporally, ultimately resulting in reduced computational complexity. Experimental simulations demonstrate that this partitioning approach outperforms the standard regular grid-based domain decomposition, achieving a reduction in computational cost while maintaining spatial and temporal load balance. The approach presented in this paper has the potential to be applied to other collective behavior simulations requiring large-scale simulations with a substantial number of individuals. |
2306.13300 | Jun Chen | Jun Chen, Jordy O Rodriguez Rincon, Gloria DeGrandi-Hoffman, Jennifer
Fewell, Jon Harrison and Yun Kang | Impacts of seasonality and parasitism on honey bee population dynamics | null | null | null | null | q-bio.PE math.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The honeybee plays an extremely important role in ecosystem stability and
diversity and in the production of bee pollinated crops. Honey bees and other
pollinators are under threat from the combined effects of nutritional stress,
parasitism, pesticides, and climate change that impact the timing, duration,
and variability of seasonal events. To understand how parasitism and
seasonality influence honey bee colonies separately and interactively, we
developed a non-autonomous nonlinear honeybee-parasite interaction differential
equation model that incorporates seasonality into the egg-laying rate of the
queen. Our theoretical results show that parasitism negatively impacts the
honey bee population either by decreasing colony size or destabilizing
population dynamics through supercritical or subcritical Hopf-bifurcations
depending on conditions. Our bifurcation analysis and simulations suggest that
seasonality alone may have positive or negative impacts on the survival of
honey bee colonies. More specifically, our study indicates that (1) the timing
of the maximum egg-laying rate seems to determine when seasonality has positive
or negative impacts; and (2) when the period of seasonality is large it can
lead to the colony collapsing. Our study further suggests that the synergistic
influences of parasitism and seasonality can lead to complicated dynamics that
may positively and negatively impact the honey bee colony's survival. Our work
partially uncovers the intrinsic effects of climate change and parasites, which
potentially provide essential insights into how best to maintain or improve a
honey bee colony's health.
| [
{
"created": "Fri, 23 Jun 2023 05:35:24 GMT",
"version": "v1"
}
] | 2023-06-26 | [
[
"Chen",
"Jun",
""
],
[
"Rincon",
"Jordy O Rodriguez",
""
],
[
"DeGrandi-Hoffman",
"Gloria",
""
],
[
"Fewell",
"Jennifer",
""
],
[
"Harrison",
"Jon",
""
],
[
"Kang",
"Yun",
""
]
] | The honeybee plays an extremely important role in ecosystem stability and diversity and in the production of bee pollinated crops. Honey bees and other pollinators are under threat from the combined effects of nutritional stress, parasitism, pesticides, and climate change that impact the timing, duration, and variability of seasonal events. To understand how parasitism and seasonality influence honey bee colonies separately and interactively, we developed a non-autonomous nonlinear honeybee-parasite interaction differential equation model that incorporates seasonality into the egg-laying rate of the queen. Our theoretical results show that parasitism negatively impacts the honey bee population either by decreasing colony size or destabilizing population dynamics through supercritical or subcritical Hopf-bifurcations depending on conditions. Our bifurcation analysis and simulations suggest that seasonality alone may have positive or negative impacts on the survival of honey bee colonies. More specifically, our study indicates that (1) the timing of the maximum egg-laying rate seems to determine when seasonality has positive or negative impacts; and (2) when the period of seasonality is large it can lead to the colony collapsing. Our study further suggests that the synergistic influences of parasitism and seasonality can lead to complicated dynamics that may positively and negatively impact the honey bee colony's survival. Our work partially uncovers the intrinsic effects of climate change and parasites, which potentially provide essential insights into how best to maintain or improve a honey bee colony's health. |
q-bio/0610005 | Shin-Ichiro Nishimura | Shin I. Nishimura and Masaki Sasai | Modulation of the reaction rate of regulating protein induces large
morphological and motional change of amoebic cell | 17 pages including 4 figures, latex source, Journal of Theoritical
Biology (in press) | null | null | null | q-bio.CB | null | Morphologies of moving amoebae are categorized into two types. One is the
``neutrophil'' type in which the long axis of cell roughly coincides with its
moving direction. This type of cell extends a leading edge at the front and
retracts a narrow tail at the rear, whose shape has been often drawn as a
typical amoeba in textbooks. The other one is the ``keratocyte'' type with
widespread lamellipodia along the front side arc. Short axis of cell in this
type roughly coincides with its moving direction. In order to understand what
kind of molecular feature causes conversion between two types of morphologies,
and how two typical morphologies are maintained, a mathematical model of
amoebic cells is developed. This model describes movement of cell and
intracellular reactions of activator, inhibitor and actin filaments in a
unified way. It is found that the producing rate of activator is a key factor
of conversion between two types. This model also explains the observed data
that the keratocye type cells tend to rapidly move along a straight line. The
neutrophil type cells move along a straight line when the moving velocity is
small, but they show fluctuated motions deviating from a line when they move as
fast as the keratocye type cells. Efficient energy consumption in the
neutrophil type cells is predicted.
| [
{
"created": "Mon, 2 Oct 2006 20:01:31 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Nishimura",
"Shin I.",
""
],
[
"Sasai",
"Masaki",
""
]
] | Morphologies of moving amoebae are categorized into two types. One is the ``neutrophil'' type in which the long axis of cell roughly coincides with its moving direction. This type of cell extends a leading edge at the front and retracts a narrow tail at the rear, whose shape has been often drawn as a typical amoeba in textbooks. The other one is the ``keratocyte'' type with widespread lamellipodia along the front side arc. Short axis of cell in this type roughly coincides with its moving direction. In order to understand what kind of molecular feature causes conversion between two types of morphologies, and how two typical morphologies are maintained, a mathematical model of amoebic cells is developed. This model describes movement of cell and intracellular reactions of activator, inhibitor and actin filaments in a unified way. It is found that the producing rate of activator is a key factor of conversion between two types. This model also explains the observed data that the keratocye type cells tend to rapidly move along a straight line. The neutrophil type cells move along a straight line when the moving velocity is small, but they show fluctuated motions deviating from a line when they move as fast as the keratocye type cells. Efficient energy consumption in the neutrophil type cells is predicted. |
2403.07147 | Akram Yazdani PhD | Akram Yazdani, Maureen Samms-Vaughan, Sepideh Saroukhani, Jan
Bressler, Manouchehr Hessabi, Amirali Tahanan, Megan L. Grove, Tanja Gangnus,
Vasanta Putluri, Abu Hena Mostafa Kamal, Nagireddy Putluri, Katherine A.
Loveland, Mohammad H. Rahbar | Metabolomic profiles in Jamaican children with and without autism
spectrum disorder | null | null | null | null | q-bio.BM | http://creativecommons.org/licenses/by/4.0/ | Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with
a wide range of behavioral and cognitive impairments. While genetic and
environmental factors are known to contribute to its etiology, the underlying
metabolic perturbations associated with ASD which can potentially connect
genetic and environmental factors, remain poorly understood. Therefore, we
conducted a metabolomic case-control study and performed a comprehensive
analysis to identify significant alterations in metabolite profiles between
children with ASD and typically developing (TD) controls. The objective of this
study is to elucidate potential metabolomic signatures associated with ASD in
children and identify specific metabolites that may serve as biomarkers for the
disorder. We conducted metabolomic profiling on plasma samples from
participants in the second phase of Epidemiological Research on Autism in
Jamaica, a cohort of 200 children with ASD and 200 TD controls (2-8 years old).
Using high-throughput liquid chromatography-mass spectrometry techniques, we
performed a targeted metabolite analysis, encompassing amino acids, lipids,
carbohydrates, and other key metabolic compounds. After quality control and
imputation of missing values, we performed univariable and multivariable
analysis using normalized metabolites while adjusting for covariates, age, sex,
socioeconomic status, and child's parish of birth. Our findings revealed unique
metabolic patterns in children with ASD for four metabolites compared to TD
controls. Notably, three of these metabolites were fatty acids, including
myristoleic acid, eicosatetraenoic acid, and octadecenoic acid. Additionally,
the amino acid sarcosine exhibited a significant association with ASD. These
findings highlight the role of metabolites in the etiology of ASD and suggest
opportunities for the development of targeted interventions.
| [
{
"created": "Mon, 11 Mar 2024 20:34:24 GMT",
"version": "v1"
}
] | 2024-03-13 | [
[
"Yazdani",
"Akram",
""
],
[
"Samms-Vaughan",
"Maureen",
""
],
[
"Saroukhani",
"Sepideh",
""
],
[
"Bressler",
"Jan",
""
],
[
"Hessabi",
"Manouchehr",
""
],
[
"Tahanan",
"Amirali",
""
],
[
"Grove",
"Megan L.",
... | Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a wide range of behavioral and cognitive impairments. While genetic and environmental factors are known to contribute to its etiology, the underlying metabolic perturbations associated with ASD which can potentially connect genetic and environmental factors, remain poorly understood. Therefore, we conducted a metabolomic case-control study and performed a comprehensive analysis to identify significant alterations in metabolite profiles between children with ASD and typically developing (TD) controls. The objective of this study is to elucidate potential metabolomic signatures associated with ASD in children and identify specific metabolites that may serve as biomarkers for the disorder. We conducted metabolomic profiling on plasma samples from participants in the second phase of Epidemiological Research on Autism in Jamaica, a cohort of 200 children with ASD and 200 TD controls (2-8 years old). Using high-throughput liquid chromatography-mass spectrometry techniques, we performed a targeted metabolite analysis, encompassing amino acids, lipids, carbohydrates, and other key metabolic compounds. After quality control and imputation of missing values, we performed univariable and multivariable analysis using normalized metabolites while adjusting for covariates, age, sex, socioeconomic status, and child's parish of birth. Our findings revealed unique metabolic patterns in children with ASD for four metabolites compared to TD controls. Notably, three of these metabolites were fatty acids, including myristoleic acid, eicosatetraenoic acid, and octadecenoic acid. Additionally, the amino acid sarcosine exhibited a significant association with ASD. These findings highlight the role of metabolites in the etiology of ASD and suggest opportunities for the development of targeted interventions. |
1507.07039 | Kimberly Glass | Kimberly Glass, John Quackenbush, Jeremy Kepner | High Performance Computing of Gene Regulatory Networks using a
Message-Passing Model | null | null | 10.1109/HPEC.2015.7322475 | null | q-bio.QM q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gene regulatory network reconstruction is a fundamental problem in
computational biology. We recently developed an algorithm, called PANDA
(Passing Attributes Between Networks for Data Assimilation), that integrates
multiple sources of 'omics data and estimates regulatory network models. This
approach was initially implemented in the C++ programming language and has
since been applied to a number of biological systems. In our current research
we are beginning to expand the algorithm to incorporate larger and most diverse
data-sets, to reconstruct networks that contain increasing numbers of elements,
and to build not only single network models, but sets of networks. In order to
accomplish these "Big Data" applications, it has become critical that we
increase the computational efficiency of the PANDA implementation. In this
paper we show how to recast PANDA's similarity equations as matrix operations.
This allows us to implement a highly readable version of the algorithm using
the MATLAB/Octave programming language. We find that the resulting M-code much
shorter (103 compared to 1128 lines) and more easily modifiable for potential
future applications. The new implementation also runs significantly faster,
with increasing efficiency as the network models increase in size. Tests
comparing the C-code and M-code versions of PANDA demonstrate that this
speed-up is on the order of 20-80 times faster for networks of similar
dimensions to those we find in current biological applications.
| [
{
"created": "Fri, 24 Jul 2015 22:51:11 GMT",
"version": "v1"
}
] | 2017-04-18 | [
[
"Glass",
"Kimberly",
""
],
[
"Quackenbush",
"John",
""
],
[
"Kepner",
"Jeremy",
""
]
] | Gene regulatory network reconstruction is a fundamental problem in computational biology. We recently developed an algorithm, called PANDA (Passing Attributes Between Networks for Data Assimilation), that integrates multiple sources of 'omics data and estimates regulatory network models. This approach was initially implemented in the C++ programming language and has since been applied to a number of biological systems. In our current research we are beginning to expand the algorithm to incorporate larger and most diverse data-sets, to reconstruct networks that contain increasing numbers of elements, and to build not only single network models, but sets of networks. In order to accomplish these "Big Data" applications, it has become critical that we increase the computational efficiency of the PANDA implementation. In this paper we show how to recast PANDA's similarity equations as matrix operations. This allows us to implement a highly readable version of the algorithm using the MATLAB/Octave programming language. We find that the resulting M-code much shorter (103 compared to 1128 lines) and more easily modifiable for potential future applications. The new implementation also runs significantly faster, with increasing efficiency as the network models increase in size. Tests comparing the C-code and M-code versions of PANDA demonstrate that this speed-up is on the order of 20-80 times faster for networks of similar dimensions to those we find in current biological applications. |
2408.05298 | Mariah C. Boudreau | Mariah C. Boudreau, Jamie A. Cohen, Laurent H\'ebert-Dufresne | Within-host infection dynamics with master equations and the method of
moments: A case study of human papillomavirus in the epithelium | 15 pages, 8 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Master equations provide researchers with the ability to track the
distribution over possible states of a system. From these equations, we can
summarize the temporal dynamics through a method of moments. These
distributions and their moments capture the stochastic nature of a system,
which is essential to study infectious diseases. In this paper, we define the
states of the system to be the number of infected cells of a given type in the
epithelium, the hollow organ tissue in the human body. Epithelium found in the
cervix provides a location for viral infections to live and persist, such as
human papillomavirus (HPV). HPV is a highly transmissible disease which most
commonly affects biological females and has the potential to progress into
cervical cancer. By defining a master equation model which tracks the infected
cell layer dynamics, information on disease extinction, progression, and viral
output can be derived from the method of moments. From this methodology and the
outcomes we glean from it, we aim to inform differing states of HPV infected
cells, and assess the effects of structural information for each outcome.
| [
{
"created": "Fri, 9 Aug 2024 18:48:06 GMT",
"version": "v1"
}
] | 2024-08-13 | [
[
"Boudreau",
"Mariah C.",
""
],
[
"Cohen",
"Jamie A.",
""
],
[
"Hébert-Dufresne",
"Laurent",
""
]
] | Master equations provide researchers with the ability to track the distribution over possible states of a system. From these equations, we can summarize the temporal dynamics through a method of moments. These distributions and their moments capture the stochastic nature of a system, which is essential to study infectious diseases. In this paper, we define the states of the system to be the number of infected cells of a given type in the epithelium, the hollow organ tissue in the human body. Epithelium found in the cervix provides a location for viral infections to live and persist, such as human papillomavirus (HPV). HPV is a highly transmissible disease which most commonly affects biological females and has the potential to progress into cervical cancer. By defining a master equation model which tracks the infected cell layer dynamics, information on disease extinction, progression, and viral output can be derived from the method of moments. From this methodology and the outcomes we glean from it, we aim to inform differing states of HPV infected cells, and assess the effects of structural information for each outcome. |
1811.11846 | Jesse Meyer | Jesse G. Meyer | Fast Proteome Identification and Quantification from Data-Dependent
Acquisition - Tandem Mass Spectrometry using Free Software Tools | null | Methods and Protocols 2019 | 10.3390/mps2010008 | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Identification of nearly all proteins in a system using data-dependent
acquisition (DDA) mass spectrometry has become routine for simple organisms,
such as bacteria and yeast. Still, quantification of the identified proteins
may be a complex process and require multiple different software packages. This
protocol describes identification and label-free quantification of proteins
from bottom-up proteomics experiments. This method can be used to quantify all
the detectable proteins in any DDA dataset collected with high-resolution
precursor scans. This protocol may be used to quantify proteome remodeling in
response to a drug treatment or a gene knockout. Notably, the method uses the
latest and fastest freely-available software, and the entire protocol can be
completed in a few hours with data from organisms with relatively small
genomes, such as yeast or bacteria.
| [
{
"created": "Wed, 28 Nov 2018 21:29:54 GMT",
"version": "v1"
}
] | 2019-01-21 | [
[
"Meyer",
"Jesse G.",
""
]
] | Identification of nearly all proteins in a system using data-dependent acquisition (DDA) mass spectrometry has become routine for simple organisms, such as bacteria and yeast. Still, quantification of the identified proteins may be a complex process and require multiple different software packages. This protocol describes identification and label-free quantification of proteins from bottom-up proteomics experiments. This method can be used to quantify all the detectable proteins in any DDA dataset collected with high-resolution precursor scans. This protocol may be used to quantify proteome remodeling in response to a drug treatment or a gene knockout. Notably, the method uses the latest and fastest freely-available software, and the entire protocol can be completed in a few hours with data from organisms with relatively small genomes, such as yeast or bacteria. |
2202.03534 | Lucas Czech | Lucas Czech, Alexandros Stamatakis, Micah Dunthorn, Pierre Barbera | Metagenomic Analysis using Phylogenetic Placement -- A Review of the
First Decade | null | null | 10.3389/fbinf.2022.871393 | null | q-bio.PE q-bio.GN | http://creativecommons.org/licenses/by/4.0/ | Phylogenetic placement refers to a family of tools and methods to analyze,
visualize, and interpret the tsunami of metagenomic sequencing data generated
by high-throughput sequencing. Compared to alternative (e. g.,
similarity-based) methods, it puts metabarcoding sequences into a phylogenetic
context using a set of known reference sequences and taking evolutionary
history into account. Thereby, one can increase the accuracy of metagenomic
surveys and eliminate the requirement for having exact or close matches with
existing sequence databases. Phylogenetic placement constitutes a valuable
analysis tool per se, but also entails a plethora of downstream tools to
interpret its results. A common use case is to analyze species communities
obtained from metagenomic sequencing, for example via taxonomic assignment,
diversity quantification, sample comparison, and identification of correlations
with environmental variables. In this review, we provide an overview over the
methods developed during the first ten years. In particular, the goals of this
review are (i) to motivate the usage of phylogenetic placement and illustrate
some of its use cases, (ii) to outline the full workflow, from raw sequences to
publishable figures, including best practices, (iii) to introduce the most
common tools and methods and their capabilities, (iv) to point out common
placement pitfalls and misconceptions,(v) to showcase typical placement-based
analyses, and how they can help to analyze, visualize, and interpret
phylogenetic placement data.
| [
{
"created": "Mon, 7 Feb 2022 21:50:54 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Mar 2022 22:41:23 GMT",
"version": "v2"
}
] | 2022-09-27 | [
[
"Czech",
"Lucas",
""
],
[
"Stamatakis",
"Alexandros",
""
],
[
"Dunthorn",
"Micah",
""
],
[
"Barbera",
"Pierre",
""
]
] | Phylogenetic placement refers to a family of tools and methods to analyze, visualize, and interpret the tsunami of metagenomic sequencing data generated by high-throughput sequencing. Compared to alternative (e. g., similarity-based) methods, it puts metabarcoding sequences into a phylogenetic context using a set of known reference sequences and taking evolutionary history into account. Thereby, one can increase the accuracy of metagenomic surveys and eliminate the requirement for having exact or close matches with existing sequence databases. Phylogenetic placement constitutes a valuable analysis tool per se, but also entails a plethora of downstream tools to interpret its results. A common use case is to analyze species communities obtained from metagenomic sequencing, for example via taxonomic assignment, diversity quantification, sample comparison, and identification of correlations with environmental variables. In this review, we provide an overview over the methods developed during the first ten years. In particular, the goals of this review are (i) to motivate the usage of phylogenetic placement and illustrate some of its use cases, (ii) to outline the full workflow, from raw sequences to publishable figures, including best practices, (iii) to introduce the most common tools and methods and their capabilities, (iv) to point out common placement pitfalls and misconceptions,(v) to showcase typical placement-based analyses, and how they can help to analyze, visualize, and interpret phylogenetic placement data. |
1012.1611 | Alexander Bershadskii | A. Bershadskii | Broken chaotic clocks of brain neurons and depression | null | null | null | null | q-bio.NC cond-mat.dis-nn nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Irregular spiking time-series obtained in vitro and in vivo from singular
brain neurons of different types of rats are analyzed by mapping to telegraph
signals. Since the neural information is coded in the length of the interspike
intervals and their positions on the time axis, this mapping is the most direct
way to map a spike train into a signal which allows a proper application of the
Fourier transform methods. This analysis shows that healthy neurons firing has
periodic and chaotic deterministic clocks while for the rats representing
genetic animal model of human depression these neuron clocks might be broken,
that results in decoherence between the depressive neurons firing. Since
depression is usually accompanied by a narrowing of consciousness this specific
decoherence can be considered as a cause of the phenomenon of the consciousness
narrowing as well. This suggestion is also supported by observation of the
large-scale chaotic coherence of the posterior piriform and entorhinal
cortices' electrical activity at transition from anesthesia to the waking state
with full consciousness.
| [
{
"created": "Tue, 7 Dec 2010 21:20:40 GMT",
"version": "v1"
},
{
"created": "Sat, 5 Mar 2011 17:28:20 GMT",
"version": "v2"
}
] | 2015-03-17 | [
[
"Bershadskii",
"A.",
""
]
] | Irregular spiking time-series obtained in vitro and in vivo from singular brain neurons of different types of rats are analyzed by mapping to telegraph signals. Since the neural information is coded in the length of the interspike intervals and their positions on the time axis, this mapping is the most direct way to map a spike train into a signal which allows a proper application of the Fourier transform methods. This analysis shows that healthy neurons firing has periodic and chaotic deterministic clocks while for the rats representing genetic animal model of human depression these neuron clocks might be broken, that results in decoherence between the depressive neurons firing. Since depression is usually accompanied by a narrowing of consciousness this specific decoherence can be considered as a cause of the phenomenon of the consciousness narrowing as well. This suggestion is also supported by observation of the large-scale chaotic coherence of the posterior piriform and entorhinal cortices' electrical activity at transition from anesthesia to the waking state with full consciousness. |
q-bio/0703013 | Ophir Flomenbom | Ophir Flomenbom and Robert J. Silbey | Utilizing the information content in two-state trajectories | The file contains: main text (+4 figures), supporting information (+9
figures), poster (1 page) | Proc. Natl. Acad. Sci. USA 103, 10907-10910 (2006) | 10.1073/pnas.0604546103 | null | q-bio.QM | null | The signal from many single molecule experiments monitoring molecular
processes, such as enzyme turnover via fluorescence and opening and closing of
ion channel via the flux of ions, consists of a time series of stochastic on
and off (or open and closed) periods, termed a two-state trajectory. This
signal reflects the dynamics in the underlying multi-substate on-off kinetic
scheme (KS) of the process. The determination of the underlying KS is difficult
and sometimes even impossible due to the loss of information in the mapping of
the mutli dimensional KS onto two dimensions. Here we introduce a new procedure
that efficiently and optimally relates the signal to all equivalent underlying
KSs. This procedure partitions the space of KSs into canonical (unique) forms
that can handle any KS, and obtains the topology and other details of the
canonical form from the data without the need for fitting. Also established are
relationships between the data and the topology of the canonical form to the
on-off connectivity of a KS. The suggested canonical forms constitute a
powerful tool in discriminating between KSs. Based on our approach, the upper
bound on the information content in two state trajectories is determined.
| [
{
"created": "Mon, 5 Mar 2007 21:19:10 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Flomenbom",
"Ophir",
""
],
[
"Silbey",
"Robert J.",
""
]
] | The signal from many single molecule experiments monitoring molecular processes, such as enzyme turnover via fluorescence and opening and closing of ion channel via the flux of ions, consists of a time series of stochastic on and off (or open and closed) periods, termed a two-state trajectory. This signal reflects the dynamics in the underlying multi-substate on-off kinetic scheme (KS) of the process. The determination of the underlying KS is difficult and sometimes even impossible due to the loss of information in the mapping of the mutli dimensional KS onto two dimensions. Here we introduce a new procedure that efficiently and optimally relates the signal to all equivalent underlying KSs. This procedure partitions the space of KSs into canonical (unique) forms that can handle any KS, and obtains the topology and other details of the canonical form from the data without the need for fitting. Also established are relationships between the data and the topology of the canonical form to the on-off connectivity of a KS. The suggested canonical forms constitute a powerful tool in discriminating between KSs. Based on our approach, the upper bound on the information content in two state trajectories is determined. |
1403.2878 | Thomas House | Thomas House | Non-Markovian stochastic epidemics in extremely heterogeneous
populations | 10 pages, 1 figure | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A feature often observed in epidemiological networks is significant
heterogeneity in degree. A popular modelling approach to this has been to
consider large populations with highly heterogeneous discrete contact rates.
This paper defines an individual-level non-Markovian stochastic process that
converges on standard ODE models of such populations in the appropriate
asymptotic limit. A generalised Sellke construction is derived for this model,
and this is then used to consider final outcomes in the case where
heterogeneity follows a truncated Zipf distribution.
| [
{
"created": "Wed, 12 Mar 2014 10:36:29 GMT",
"version": "v1"
}
] | 2014-03-13 | [
[
"House",
"Thomas",
""
]
] | A feature often observed in epidemiological networks is significant heterogeneity in degree. A popular modelling approach to this has been to consider large populations with highly heterogeneous discrete contact rates. This paper defines an individual-level non-Markovian stochastic process that converges on standard ODE models of such populations in the appropriate asymptotic limit. A generalised Sellke construction is derived for this model, and this is then used to consider final outcomes in the case where heterogeneity follows a truncated Zipf distribution. |
2110.00668 | Harrison Ritz | Harrison Ritz, Xiamin Leng, and Amitai Shenhav | Cognitive control as a multivariate optimization problem | null | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by-sa/4.0/ | Research has characterized the various forms cognitive control can take,
including enhancement of goal-relevant information, suppression of
goal-irrelevant information, and overall inhibition of potential responses, and
has identified computations and neural circuits that underpin this multitude of
control types. Studies have also identified a wide range of situations that
elicit adjustments in control allocation (e.g., those eliciting signals
indicating an error or increased processing conflict), but the rules governing
when a given situation will give rise to a given control adjustment remain
poorly understood. Significant progress has recently been made on this front by
casting the allocation of control as a decision-making problem, and developing
unifying and normative models that prescribe when and how a change in
incentives and task demands will result in changes in a given form of control.
Despite their successes, these models, and the experiments that have been
developed to test them, have yet to face their greatest challenge: deciding how
to allocate control across the multiplicity of control signals that one could
engage at any given time. Here, we will lay out the complexities of the inverse
problem inherent to cognitive control allocation, and their close parallels to
inverse problems within motor control (e.g., choosing between redundant limb
movements). We discuss existing solutions to motor control`s inverse problems
drawn from optimal control theory, which have proposed that effort costs act to
regularize actions and transform motor planning into a well-posed problem.
These same principles may help shed light on how our brains optimize over
complex control configuration, while providing a new normative perspective on
the origins of mental effort.
| [
{
"created": "Fri, 1 Oct 2021 22:14:51 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Jan 2022 04:43:50 GMT",
"version": "v2"
}
] | 2022-01-04 | [
[
"Ritz",
"Harrison",
""
],
[
"Leng",
"Xiamin",
""
],
[
"Shenhav",
"Amitai",
""
]
] | Research has characterized the various forms cognitive control can take, including enhancement of goal-relevant information, suppression of goal-irrelevant information, and overall inhibition of potential responses, and has identified computations and neural circuits that underpin this multitude of control types. Studies have also identified a wide range of situations that elicit adjustments in control allocation (e.g., those eliciting signals indicating an error or increased processing conflict), but the rules governing when a given situation will give rise to a given control adjustment remain poorly understood. Significant progress has recently been made on this front by casting the allocation of control as a decision-making problem, and developing unifying and normative models that prescribe when and how a change in incentives and task demands will result in changes in a given form of control. Despite their successes, these models, and the experiments that have been developed to test them, have yet to face their greatest challenge: deciding how to allocate control across the multiplicity of control signals that one could engage at any given time. Here, we will lay out the complexities of the inverse problem inherent to cognitive control allocation, and their close parallels to inverse problems within motor control (e.g., choosing between redundant limb movements). We discuss existing solutions to motor control`s inverse problems drawn from optimal control theory, which have proposed that effort costs act to regularize actions and transform motor planning into a well-posed problem. These same principles may help shed light on how our brains optimize over complex control configuration, while providing a new normative perspective on the origins of mental effort. |
q-bio/0605016 | Ellen Baake | Natali Zint, Ellen Baake and Frank den Hollander | How T-cells use large deviations to recognize foreign antigens | 16 pages, 6 figures; minor revision, new simulations; J Math Biol.,
in press | J. Math. Biol. 57 (2008), 841-861 | null | null | q-bio.SC math.PR q-bio.MN | null | A stochastic model for the activation of T-cells is analysed. T-cells are
part of the immune system and recognize foreign antigens against a background
of the body's own molecules. The model under consideration is a slight
generalization of a model introduced by Van den Berg, Rand and Burroughs in
2001, and is capable of explaining how this recognition works on the basis of
rare stochastic events. With the help of a refined large deviation theorem and
numerical evaluation it is shown that, for a wide range of parameters, T-cells
can distinguish reliably between foreign antigens and self-antigens.
| [
{
"created": "Thu, 11 May 2006 09:01:59 GMT",
"version": "v1"
},
{
"created": "Thu, 15 May 2008 08:47:37 GMT",
"version": "v2"
}
] | 2009-02-19 | [
[
"Zint",
"Natali",
""
],
[
"Baake",
"Ellen",
""
],
[
"Hollander",
"Frank den",
""
]
] | A stochastic model for the activation of T-cells is analysed. T-cells are part of the immune system and recognize foreign antigens against a background of the body's own molecules. The model under consideration is a slight generalization of a model introduced by Van den Berg, Rand and Burroughs in 2001, and is capable of explaining how this recognition works on the basis of rare stochastic events. With the help of a refined large deviation theorem and numerical evaluation it is shown that, for a wide range of parameters, T-cells can distinguish reliably between foreign antigens and self-antigens. |
1311.1171 | Stuart Borrett Stuart Borrett | David E. Hines, Jessica A. Lisa, Bongkeun Song, Craig R. Tobias,
Stuart R. Borrett | Estimating the effects of sea level rise on coupled estuarine nitrogen
cycling processes through comparative network analysis | 18 pages, 2 tables, 8 figures, 1 web appendix with two ecosystem
network models | Marine Ecology Progress Series 524: 137-154 | 10.3354/meps11187 | null | q-bio.QM q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Nitrogen (N) removal from estuaries is driven in part by sedimentary
microbial processes. The processes of denitrification and anaerobic ammonium
oxidation (anammox) remove N from estuaries by producing di-nitrogen gas, and
each can be coupled to N recycling pathways such as nitrification and
dissimilatory nitrate reduction to ammonium (DNRA). Environmental conditions in
estuaries influence sedimentary N cycling processes; therefore, seawater
intrusion may affect the coupling of N cycling processes in the freshwater
portions of estuaries. This study investigated the potential effects of
seawater intrusion on these process couplings through a comparative modeling
approach. We applied environ analysis, a form of ecological network analysis,
to two N cycling mass-balance network models constructed at freshwater
(oligohaline) and saltwater (polyhaline) sites in the Cape Fear River Estuary,
North Carolina. We used a space-for-time substitution to predict the effects of
seawater intrusion on the sedimentary N cycle. Further, we conducted an
uncertainty analysis using linear inverse modeling to evaluate the effects of
parameterization uncertainty on model results. Nitrification coupled to both
denitrification and anammox was 2.5 times greater in the oligohaline model,
while DNRA coupled to anammox was 2.7 times greater in the polyhaline model.
However, the total amount of N2 gas produced relative to the nitrogen inputs to
each network was 4.7% and 4.6% at the oligohaline and polyhaline sites,
respectively. These findings suggest that changes in water chemistry from
seawater intrusion may favor direct over coupled nitrogen removal, but may not
substantially change the N removal capacity of the sedimentary microbial
processes.
| [
{
"created": "Tue, 5 Nov 2013 19:32:07 GMT",
"version": "v1"
}
] | 2015-04-16 | [
[
"Hines",
"David E.",
""
],
[
"Lisa",
"Jessica A.",
""
],
[
"Song",
"Bongkeun",
""
],
[
"Tobias",
"Craig R.",
""
],
[
"Borrett",
"Stuart R.",
""
]
] | Nitrogen (N) removal from estuaries is driven in part by sedimentary microbial processes. The processes of denitrification and anaerobic ammonium oxidation (anammox) remove N from estuaries by producing di-nitrogen gas, and each can be coupled to N recycling pathways such as nitrification and dissimilatory nitrate reduction to ammonium (DNRA). Environmental conditions in estuaries influence sedimentary N cycling processes; therefore, seawater intrusion may affect the coupling of N cycling processes in the freshwater portions of estuaries. This study investigated the potential effects of seawater intrusion on these process couplings through a comparative modeling approach. We applied environ analysis, a form of ecological network analysis, to two N cycling mass-balance network models constructed at freshwater (oligohaline) and saltwater (polyhaline) sites in the Cape Fear River Estuary, North Carolina. We used a space-for-time substitution to predict the effects of seawater intrusion on the sedimentary N cycle. Further, we conducted an uncertainty analysis using linear inverse modeling to evaluate the effects of parameterization uncertainty on model results. Nitrification coupled to both denitrification and anammox was 2.5 times greater in the oligohaline model, while DNRA coupled to anammox was 2.7 times greater in the polyhaline model. However, the total amount of N2 gas produced relative to the nitrogen inputs to each network was 4.7% and 4.6% at the oligohaline and polyhaline sites, respectively. These findings suggest that changes in water chemistry from seawater intrusion may favor direct over coupled nitrogen removal, but may not substantially change the N removal capacity of the sedimentary microbial processes. |
2310.15857 | Sean Brown | Sean M. Brown, Christopher Mayer-Bacon, Stephen Freeland | Xeno Amino Acids: A look into biochemistry as we don't know it | Submitted to Life (ISSN 2075-1729), 26 pages (without references), 8
figures, 1 table, 1 box | Life, 13(12), 2281 (2023) | 10.3390/life13122281 | null | q-bio.BM q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Would another origin of life resemble Earth's biochemical use of amino acids?
Here we review current knowledge at three levels: 1) Could other classes of
chemical structure serve as building blocks for biopolymer structure and
catalysis? Amino acids now seem both readily available to, and a plausible
chemical attractor for, life as we don't know it. Amino acids thus remain
important and tractable targets for astrobiological research. 2) If amino acids
are used, would we expect the same L-alpha-structural subclass used by life?
Despite numerous ideas, it is not clear why life favors L-enantiomers. It seems
clearer, however, why life on Earth uses the shortest possible (alpha-) amino
acid backbone, and why each carries only one side chain. However, assertions
that other backbones are physicochemically impossible have relaxed into
arguments that they are disadvantageous. 3) Would we expect a similar set of
side chains to those within the genetic code? Not only do many plausible
alternatives exist and evidence exists for both evolutionary advantage and
physicochemical constraint for those encoded by life. Overall, as focus shifts
from amino acids as a chemical class to specific side chains used by post-LUCA
biology, the probable role of physicochemical constraint diminishes relative to
that of biological evolution. Exciting opportunities now present themselves for
laboratory work and computing to explore how changing the amino acid alphabet
alters the universe of protein folds. Near-term milestones include: a)
expanding evidence about amino acids as attractors within chemical evolution;
b) extending characterization of other backbones relative to biological
proteins; c) merging computing and laboratory explorations of structures and
functions unlocked by xeno peptides.
| [
{
"created": "Tue, 24 Oct 2023 14:15:55 GMT",
"version": "v1"
},
{
"created": "Mon, 30 Oct 2023 14:39:08 GMT",
"version": "v2"
}
] | 2024-06-04 | [
[
"Brown",
"Sean M.",
""
],
[
"Mayer-Bacon",
"Christopher",
""
],
[
"Freeland",
"Stephen",
""
]
] | Would another origin of life resemble Earth's biochemical use of amino acids? Here we review current knowledge at three levels: 1) Could other classes of chemical structure serve as building blocks for biopolymer structure and catalysis? Amino acids now seem both readily available to, and a plausible chemical attractor for, life as we don't know it. Amino acids thus remain important and tractable targets for astrobiological research. 2) If amino acids are used, would we expect the same L-alpha-structural subclass used by life? Despite numerous ideas, it is not clear why life favors L-enantiomers. It seems clearer, however, why life on Earth uses the shortest possible (alpha-) amino acid backbone, and why each carries only one side chain. However, assertions that other backbones are physicochemically impossible have relaxed into arguments that they are disadvantageous. 3) Would we expect a similar set of side chains to those within the genetic code? Not only do many plausible alternatives exist and evidence exists for both evolutionary advantage and physicochemical constraint for those encoded by life. Overall, as focus shifts from amino acids as a chemical class to specific side chains used by post-LUCA biology, the probable role of physicochemical constraint diminishes relative to that of biological evolution. Exciting opportunities now present themselves for laboratory work and computing to explore how changing the amino acid alphabet alters the universe of protein folds. Near-term milestones include: a) expanding evidence about amino acids as attractors within chemical evolution; b) extending characterization of other backbones relative to biological proteins; c) merging computing and laboratory explorations of structures and functions unlocked by xeno peptides. |
1009.2470 | Marta Luksza | Marta {\L}uksza, Michael L\"assig and Johannes Berg | Significance analysis and statistical mechanics: an application to
clustering | to appear in Phys. Rev. Lett | null | 10.1103/PhysRevLett.105.220601 | null | q-bio.MN cond-mat.stat-mech q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses the statistical significance of structures in random
data: Given a set of vectors and a measure of mutual similarity, how likely
does a subset of these vectors form a cluster with enhanced similarity among
its elements? The computation of this cluster p-value for randomly distributed
vectors is mapped onto a well-defined problem of statistical mechanics. We
solve this problem analytically, establishing a connection between the physics
of quenched disorder and multiple testing statistics in clustering and related
problems. In an application to gene expression data, we find a remarkable link
between the statistical significance of a cluster and the functional
relationships between its genes.
| [
{
"created": "Mon, 13 Sep 2010 18:25:05 GMT",
"version": "v1"
}
] | 2015-05-19 | [
[
"Łuksza",
"Marta",
""
],
[
"Lässig",
"Michael",
""
],
[
"Berg",
"Johannes",
""
]
] | This paper addresses the statistical significance of structures in random data: Given a set of vectors and a measure of mutual similarity, how likely does a subset of these vectors form a cluster with enhanced similarity among its elements? The computation of this cluster p-value for randomly distributed vectors is mapped onto a well-defined problem of statistical mechanics. We solve this problem analytically, establishing a connection between the physics of quenched disorder and multiple testing statistics in clustering and related problems. In an application to gene expression data, we find a remarkable link between the statistical significance of a cluster and the functional relationships between its genes. |
1412.5982 | Anna Ochab-Marcinek | Anna Ochab-Marcinek and Marcin Tabaka | Transcriptional leakage versus noise: A simple mechanism of conversion
between binary and graded response in autoregulated genes | 8 pages, 6 figures, published in Physical Review E,
http://link.aps.org/doi/10.1103/PhysRevE.91.012704 , copyright APS. Added
missing explanations of the symbols in Tab. 1. Corrected "inactive", "active"
in the text. Added missing "transcriptional" in the abstract | null | 10.1103/PhysRevE.91.012704 | null | q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the response of an autoregulated gene to a range of concentrations
of signal molecules. We show that transcriptional leakage and noise due to
translational bursting have the opposite effects. In a positively autoregulated
gene, increasing the noise converts the response from graded to binary, while
increasing the leakage converts the response from binary to graded. Our
findings support the hypothesis that, being a common phenomenon, leaky
expression may be a relatively easy way for evolutionary tuning of the type of
gene response without changing the type of regulation from positive to
negative.
| [
{
"created": "Thu, 18 Dec 2014 18:12:01 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Jan 2015 14:43:12 GMT",
"version": "v2"
},
{
"created": "Fri, 16 Jan 2015 16:48:52 GMT",
"version": "v3"
}
] | 2015-01-19 | [
[
"Ochab-Marcinek",
"Anna",
""
],
[
"Tabaka",
"Marcin",
""
]
] | We study the response of an autoregulated gene to a range of concentrations of signal molecules. We show that transcriptional leakage and noise due to translational bursting have the opposite effects. In a positively autoregulated gene, increasing the noise converts the response from graded to binary, while increasing the leakage converts the response from binary to graded. Our findings support the hypothesis that, being a common phenomenon, leaky expression may be a relatively easy way for evolutionary tuning of the type of gene response without changing the type of regulation from positive to negative. |
0904.2327 | Rainer Klages | Peter Dieterich (1), Rainer Klages (2), Roland Preuss (3), Albrecht
Schwab (4) ((1) TU Dresden, Germany (2) Queen Mary University of London, UK
(3) Max-Planck-Institute for Plasma Physics, Garching, Germany (4) University
of Muenster, Germany) | Anomalous dynamics of cell migration | 20 pages, 3 figures, 1 table | PNAS 105, 459 (2008) | 10.1073/pnas.0707603105 | null | q-bio.CB cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cell movement, for example during embryogenesis or tumor metastasis, is a
complex dynamical process resulting from an intricate interplay of multiple
components of the cellular migration machinery. At first sight, the paths of
migrating cells resemble those of thermally driven Brownian particles. However,
cell migration is an active biological process putting a characterization in
terms of normal Brownian motion into question. By analyzing the trajectories of
wildtype and mutated epithelial (MDCK-F) cells we show experimentally that
anomalous dynamics characterizes cell migration. A superdiffusive increase of
the mean squared displacement, non-Gaussian spatial probability distributions,
and power-law decays of the velocity autocorrelations are the basis for this
interpretation. Almost all results can be explained with a fractional Klein-
Kramers equation allowing the quantitative classification of cell migration by
a few parameters. Thereby it discloses the influence and relative importance of
individual components of the cellular migration apparatus to the behavior of
the cell as a whole.
| [
{
"created": "Wed, 15 Apr 2009 14:11:48 GMT",
"version": "v1"
}
] | 2009-04-17 | [
[
"Dieterich",
"Peter",
""
],
[
"Klages",
"Rainer",
""
],
[
"Preuss",
"Roland",
""
],
[
"Schwab",
"Albrecht",
""
]
] | Cell movement, for example during embryogenesis or tumor metastasis, is a complex dynamical process resulting from an intricate interplay of multiple components of the cellular migration machinery. At first sight, the paths of migrating cells resemble those of thermally driven Brownian particles. However, cell migration is an active biological process putting a characterization in terms of normal Brownian motion into question. By analyzing the trajectories of wildtype and mutated epithelial (MDCK-F) cells we show experimentally that anomalous dynamics characterizes cell migration. A superdiffusive increase of the mean squared displacement, non-Gaussian spatial probability distributions, and power-law decays of the velocity autocorrelations are the basis for this interpretation. Almost all results can be explained with a fractional Klein- Kramers equation allowing the quantitative classification of cell migration by a few parameters. Thereby it discloses the influence and relative importance of individual components of the cellular migration apparatus to the behavior of the cell as a whole. |
2201.02703 | Pablo Carlos L\'opez Dr. | Pablo Carlos L\'opez V\'azquez and Gilberto S\'anchez Gonz\'alez and
Jorge Mart\'inez Ortega and Renato Salom\'on Arroyo Duarte | Stochastic epidemiological model: Modeling the SARS-CoV-2 spreading in
Mexico | 14 pages, 7 figures | null | 10.1371/journal.pone.0275216 | null | q-bio.PE stat.AP | http://creativecommons.org/licenses/by/4.0/ | In this paper we model the spreading of the SARS-CoV-2 in Mexico by
introducing a new stochastic approximation constructed from first principles,
structured on the basis of a Latent-Infectious- (Recovered or Deceased)
(LI(RD)) compartmental approximation, where the number of new infected
individuals caused by a single infectious individual per unit time (a day), is
a random variable of a Poisson distribution and whose parameter is modulated
through a weight-like time-dependent function. The weight function serves to
introduce a time dependence to the average number of new infections and as we
will show, this information can be extracted from empirical data, giving to the
model self-consistency and provides a tool to study information about periodic
patterns encoded in the epidemiological dynamics
| [
{
"created": "Fri, 7 Jan 2022 22:54:44 GMT",
"version": "v1"
}
] | 2023-01-11 | [
[
"Vázquez",
"Pablo Carlos López",
""
],
[
"González",
"Gilberto Sánchez",
""
],
[
"Ortega",
"Jorge Martínez",
""
],
[
"Duarte",
"Renato Salomón Arroyo",
""
]
] | In this paper we model the spreading of the SARS-CoV-2 in Mexico by introducing a new stochastic approximation constructed from first principles, structured on the basis of a Latent-Infectious- (Recovered or Deceased) (LI(RD)) compartmental approximation, where the number of new infected individuals caused by a single infectious individual per unit time (a day), is a random variable of a Poisson distribution and whose parameter is modulated through a weight-like time-dependent function. The weight function serves to introduce a time dependence to the average number of new infections and as we will show, this information can be extracted from empirical data, giving to the model self-consistency and provides a tool to study information about periodic patterns encoded in the epidemiological dynamics |
1110.1412 | Eleni Katifori | Eleni Katifori and Marcelo O. Magnasco | Quantifying loopy network architectures | 17 pages, 8 figures. During preparation of this manuscript the
authors became aware of the work of Mileyko at al., concurrently submitted
for publication | null | 10.1371/journal.pone.0037994 | null | q-bio.QM cond-mat.stat-mech nlin.AO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Biology presents many examples of planar distribution and structural networks
having dense sets of closed loops. An archetype of this form of network
organization is the vasculature of dicotyledonous leaves, which showcases a
hierarchically-nested architecture containing closed loops at many different
levels. Although a number of methods have been proposed to measure aspects of
the structure of such networks, a robust metric to quantify their hierarchical
organization is still lacking. We present an algorithmic framework, the
hierarchical loop decomposition, that allows mapping loopy networks to binary
trees, preserving in the connectivity of the trees the architecture of the
original graph. We apply this framework to investigate computer generated
graphs, such as artificial models and optimal distribution networks, as well as
natural graphs extracted from digitized images of dicotyledonous leaves and
vasculature of rat cerebral neocortex. We calculate various metrics based on
the Asymmetry, the cumulative size distribution and the Strahler bifurcation
ratios of the corresponding trees and discuss the relationship of these
quantities to the architectural organization of the original graphs. This
algorithmic framework decouples the geometric information (exact location of
edges and nodes) from the metric topology (connectivity and edge weight) and it
ultimately allows us to perform a quantitative statistical comparison between
predictions of theoretical models and naturally occurring loopy graphs.
| [
{
"created": "Thu, 6 Oct 2011 23:26:57 GMT",
"version": "v1"
}
] | 2015-05-30 | [
[
"Katifori",
"Eleni",
""
],
[
"Magnasco",
"Marcelo O.",
""
]
] | Biology presents many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture containing closed loops at many different levels. Although a number of methods have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework, the hierarchical loop decomposition, that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated graphs, such as artificial models and optimal distribution networks, as well as natural graphs extracted from digitized images of dicotyledonous leaves and vasculature of rat cerebral neocortex. We calculate various metrics based on the Asymmetry, the cumulative size distribution and the Strahler bifurcation ratios of the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information (exact location of edges and nodes) from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs. |
2306.08564 | Raja Marjieh | Raja Marjieh, Nori Jacoby, Joshua C. Peterson, Thomas L. Griffiths | The Universal Law of Generalization Holds for Naturalistic Stimuli | 36 pages, 6 figures | null | null | null | q-bio.NC cs.AI stat.AP | http://creativecommons.org/licenses/by/4.0/ | Shepard's universal law of generalization is a remarkable hypothesis about
how intelligent organisms should perceive similarity. In its broadest form, the
universal law states that the level of perceived similarity between a pair of
stimuli should decay as a concave function of their distance when embedded in
an appropriate psychological space. While extensively studied, evidence in
support of the universal law has relied on low-dimensional stimuli and small
stimulus sets that are very different from their real-world counterparts. This
is largely because pairwise comparisons -- as required for similarity judgments
-- scale quadratically in the number of stimuli. We provide direct evidence for
the universal law in a naturalistic high-dimensional regime by analyzing an
existing dataset of 214,200 human similarity judgments and a newly collected
dataset of 390,819 human generalization judgments (N=2406 US participants)
across three sets of natural images.
| [
{
"created": "Wed, 14 Jun 2023 15:17:48 GMT",
"version": "v1"
}
] | 2023-06-16 | [
[
"Marjieh",
"Raja",
""
],
[
"Jacoby",
"Nori",
""
],
[
"Peterson",
"Joshua C.",
""
],
[
"Griffiths",
"Thomas L.",
""
]
] | Shepard's universal law of generalization is a remarkable hypothesis about how intelligent organisms should perceive similarity. In its broadest form, the universal law states that the level of perceived similarity between a pair of stimuli should decay as a concave function of their distance when embedded in an appropriate psychological space. While extensively studied, evidence in support of the universal law has relied on low-dimensional stimuli and small stimulus sets that are very different from their real-world counterparts. This is largely because pairwise comparisons -- as required for similarity judgments -- scale quadratically in the number of stimuli. We provide direct evidence for the universal law in a naturalistic high-dimensional regime by analyzing an existing dataset of 214,200 human similarity judgments and a newly collected dataset of 390,819 human generalization judgments (N=2406 US participants) across three sets of natural images. |
1806.02893 | William T Redman | William T Redman | An O(n) method of calculating Kendall correlations of spike trains | 7 pages, 1 figure, 1 table | PLoS ONE (2019) 14(2): e0212190 | 10.1371/journal.pone.0212190 | null | q-bio.QM q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | The ability to record from increasingly large numbers of neurons, and the
increasing attention being paid to large scale neural network simulations,
demands computationally fast algorithms to compute relevant statistical
measures. We present an O(n) algorithm for calculating the Kendall correlation
of spike trains, a correlation measure that is becoming especially recognized
as an important tool in neuroscience. We show that our method is around 50
times faster than the O (n ln n) method which is a current standard for quickly
computing the Kendall correlation. In addition to providing a faster algorithm,
we emphasize the role that taking the specific nature of spike trains had on
reducing the run time. We imagine that there are many other useful algorithms
that can be even more significantly sped up when taking this into
consideration. A MATLAB function executing the method described here has been
made freely available on-line.
| [
{
"created": "Thu, 7 Jun 2018 20:49:07 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Jan 2020 06:38:40 GMT",
"version": "v2"
},
{
"created": "Tue, 25 Feb 2020 05:35:41 GMT",
"version": "v3"
}
] | 2020-02-26 | [
[
"Redman",
"William T",
""
]
] | The ability to record from increasingly large numbers of neurons, and the increasing attention being paid to large scale neural network simulations, demands computationally fast algorithms to compute relevant statistical measures. We present an O(n) algorithm for calculating the Kendall correlation of spike trains, a correlation measure that is becoming especially recognized as an important tool in neuroscience. We show that our method is around 50 times faster than the O (n ln n) method which is a current standard for quickly computing the Kendall correlation. In addition to providing a faster algorithm, we emphasize the role that taking the specific nature of spike trains had on reducing the run time. We imagine that there are many other useful algorithms that can be even more significantly sped up when taking this into consideration. A MATLAB function executing the method described here has been made freely available on-line. |
2108.06610 | Harisankar Sadasivan | Tim Dunn, Harisankar Sadasivan, Jack Wadden, Kush Goliya, Kuan-Yu
Chen, Reetuparna Das, David Blaauw, Satish Narayanasamy | SquiggleFilter: An Accelerator for Portable Virus Detection | https://micro2021ae.hotcrp.com/paper/12?cap=012aOJj-0U08_9o | null | null | null | q-bio.GN | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The MinION is a recent-to-market handheld nanopore sequencer. It can be used
to determine the whole genome of a target virus in a biological sample. Its
Read Until feature allows us to skip sequencing a majority of non-target reads
(DNA/RNA fragments), which constitutes more than 99% of all reads in a typical
sample. However, it does not have any on-board computing, which significantly
limits its portability.
We analyze the performance of a Read Until metagenomic pipeline for detecting
target viruses and identifying strain-specific mutations. We find new sources
of performance bottlenecks (basecaller in classification of a read) that are
not addressed by past genomics accelerators.
We present SquiggleFilter, a novel hardware accelerated dynamic time warping
(DTW) based filter that directly analyzes MinION's raw squiggles and filters
everything except target viral reads, thereby avoiding the expensive
basecalling step. We show that our 14.3W 13.25mm2 accelerator has 274X greater
throughput and 3481X lower latency than existing GPU-based solutions while
consuming half the power, enabling Read Until for the next generation of
nanopore sequencers.
| [
{
"created": "Sat, 14 Aug 2021 20:35:27 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Sep 2021 16:10:09 GMT",
"version": "v2"
}
] | 2021-09-24 | [
[
"Dunn",
"Tim",
""
],
[
"Sadasivan",
"Harisankar",
""
],
[
"Wadden",
"Jack",
""
],
[
"Goliya",
"Kush",
""
],
[
"Chen",
"Kuan-Yu",
""
],
[
"Das",
"Reetuparna",
""
],
[
"Blaauw",
"David",
""
],
[
"Nara... | The MinION is a recent-to-market handheld nanopore sequencer. It can be used to determine the whole genome of a target virus in a biological sample. Its Read Until feature allows us to skip sequencing a majority of non-target reads (DNA/RNA fragments), which constitutes more than 99% of all reads in a typical sample. However, it does not have any on-board computing, which significantly limits its portability. We analyze the performance of a Read Until metagenomic pipeline for detecting target viruses and identifying strain-specific mutations. We find new sources of performance bottlenecks (basecaller in classification of a read) that are not addressed by past genomics accelerators. We present SquiggleFilter, a novel hardware accelerated dynamic time warping (DTW) based filter that directly analyzes MinION's raw squiggles and filters everything except target viral reads, thereby avoiding the expensive basecalling step. We show that our 14.3W 13.25mm2 accelerator has 274X greater throughput and 3481X lower latency than existing GPU-based solutions while consuming half the power, enabling Read Until for the next generation of nanopore sequencers. |
1707.07145 | C\'esar Parra-Rojas | C\'esar Parra-Rojas, Alan J. McKane | Reduction of a metapopulation genetic model to an effective one island
model | 16 pages, 4 figures. Supplementary material: 22 pages, 3 figures | Europhys. Lett. 122, 18001 (2018) | 10.1209/0295-5075/122/18001 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore a model of metapopulation genetics which is based on a more
ecologically motivated approach than is frequently used in population genetics.
The size of the population is regulated by competition between individuals,
rather than by artificially imposing a fixed population size. The increased
complexity of the model is managed by employing techniques often used in the
physical sciences, namely exploiting time-scale separation to eliminate fast
variables and then constructing an effective model from the slow modes.
Remarkably, an initial model with 2$\mathcal{D}$ variables, where $\mathcal{D}$
is the number of islands in the metapopulation, can be reduced to a model with
a single variable. We analyze this effective model and show that the
predictions for the probability of fixation of the alleles and the mean time to
fixation agree well with those found from numerical simulations of the original
model.
| [
{
"created": "Sat, 22 Jul 2017 11:58:12 GMT",
"version": "v1"
},
{
"created": "Sun, 27 May 2018 11:00:41 GMT",
"version": "v2"
}
] | 2018-05-29 | [
[
"Parra-Rojas",
"César",
""
],
[
"McKane",
"Alan J.",
""
]
] | We explore a model of metapopulation genetics which is based on a more ecologically motivated approach than is frequently used in population genetics. The size of the population is regulated by competition between individuals, rather than by artificially imposing a fixed population size. The increased complexity of the model is managed by employing techniques often used in the physical sciences, namely exploiting time-scale separation to eliminate fast variables and then constructing an effective model from the slow modes. Remarkably, an initial model with 2$\mathcal{D}$ variables, where $\mathcal{D}$ is the number of islands in the metapopulation, can be reduced to a model with a single variable. We analyze this effective model and show that the predictions for the probability of fixation of the alleles and the mean time to fixation agree well with those found from numerical simulations of the original model. |
1912.05057 | Julia Shore | Julia A. Shore, Barbara R. Holland, Jeremy G. Sumner, Kay Nieselt and
Peter R. Wills | The ancient Operational Code is embedded in the amino acid substitution
matrix and aaRS phylogenies | null | Journal of molecular evolution, 1-15 (2019) | 10.1007/s00239-019-09918-z | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The underlying structure of the canonical amino acid substitution matrix
(aaSM) is examined by considering stepwise improvements in the differential
recognition of amino acids according to their chemical properties during the
branching history of the two aminoacyl-tRNA synthetase (aaRS) superfamilies.
The evolutionary expansion of the genetic code is described by a simple
parameterization of the aaSM, in which (i) the number of distinguishable amino
acid types, (ii) the matrix dimension, and (iii) the number of parameters, each
increases by one for each bifurcation in an aaRS phylogeny. Parameterized
matrices corresponding to trees in which the size of an amino acid sidechain is
the only discernible property behind its categorization as a substrate,
exclusively for a Class I or II aaRS, provide a significantly better fit to
empirically determined aaSM than trees with random bifurcation patterns. A
second split between polar and nonpolar amino acids in each Class effects a
vastly greater further improvement. The earliest Class-separated epochs in the
phylogenies of the aaRS reflect these enzymes' capability to distinguish tRNAs
through the recognition of acceptor stem identity elements via the minor (Class
I) and major (Class II) helical grooves, which is how the ancient Operational
Code functioned. The advent of tRNA recognition using the anticodon loop
supports the evolution of the optimal map of amino acid chemistry found in the
later Genetic Code, an essentially digital categorization, in which polarity is
the major functional property, compensating for the unrefined, haphazard
differentiation of amino acids achieved by the Operational Code.
| [
{
"created": "Wed, 11 Dec 2019 00:06:30 GMT",
"version": "v1"
}
] | 2019-12-12 | [
[
"Shore",
"Julia A.",
""
],
[
"Holland",
"Barbara R.",
""
],
[
"Sumner",
"Jeremy G.",
""
],
[
"Nieselt",
"Kay",
""
],
[
"Wills",
"Peter R.",
""
]
] | The underlying structure of the canonical amino acid substitution matrix (aaSM) is examined by considering stepwise improvements in the differential recognition of amino acids according to their chemical properties during the branching history of the two aminoacyl-tRNA synthetase (aaRS) superfamilies. The evolutionary expansion of the genetic code is described by a simple parameterization of the aaSM, in which (i) the number of distinguishable amino acid types, (ii) the matrix dimension, and (iii) the number of parameters, each increases by one for each bifurcation in an aaRS phylogeny. Parameterized matrices corresponding to trees in which the size of an amino acid sidechain is the only discernible property behind its categorization as a substrate, exclusively for a Class I or II aaRS, provide a significantly better fit to empirically determined aaSM than trees with random bifurcation patterns. A second split between polar and nonpolar amino acids in each Class effects a vastly greater further improvement. The earliest Class-separated epochs in the phylogenies of the aaRS reflect these enzymes' capability to distinguish tRNAs through the recognition of acceptor stem identity elements via the minor (Class I) and major (Class II) helical grooves, which is how the ancient Operational Code functioned. The advent of tRNA recognition using the anticodon loop supports the evolution of the optimal map of amino acid chemistry found in the later Genetic Code, an essentially digital categorization, in which polarity is the major functional property, compensating for the unrefined, haphazard differentiation of amino acids achieved by the Operational Code. |
q-bio/0604012 | Serge Smidtas | Serge Smidtas, Vincent Schachter, Francois Kepes | The adaptive filter of the yeast galactose pathway | null | J. Theor. Biol. 2005 | null | null | q-bio.MN q-bio.CB | null | In the yeast Saccharomyces cerevisiae, the interplay between galactose,
Gal3p, Gal80p and Gal4p determines the transcriptional status of the genes
required for galactose utilization. After an increase in galactose
concentration, galactose molecules bind onto Gal3p. This event leads via Gal80p
to the activation of Gal4p, which then induces GAL3 and GAL80 gene
transcription. Here we propose a qualitative dynamic model, whereby these
molecular interaction events represent the first two stages of a functional
feedback loop that closes with the capture of activated Gal4p by newly
synthesized Gal3p and Gal80p, decreasing transcriptional activation and
creating again the protein complex that can bind incoming galactose molecules.
Based on the differential time scales of faster protein interactions versus
slower biosynthetic steps, this feedback loop functions as a derivative filter
where galactose is the input step signal, and released Gal4p is the output
derivative signal. One advantage of such a derivative filter is that GAL genes
are expressed in proportion to the cellular requirement. Furthermore, this
filter adaptively protects the cellular receptors from saturation by galactose,
allowing cells to remain sensitive to variations in galactose concentrations
rather than to absolute concentrations. Finally, this feedback loop, by
allowing phosphorylation of some active Gal4p, may be essential to initiate the
subsequent long-term response.
| [
{
"created": "Mon, 10 Apr 2006 11:02:16 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Smidtas",
"Serge",
""
],
[
"Schachter",
"Vincent",
""
],
[
"Kepes",
"Francois",
""
]
] | In the yeast Saccharomyces cerevisiae, the interplay between galactose, Gal3p, Gal80p and Gal4p determines the transcriptional status of the genes required for galactose utilization. After an increase in galactose concentration, galactose molecules bind onto Gal3p. This event leads via Gal80p to the activation of Gal4p, which then induces GAL3 and GAL80 gene transcription. Here we propose a qualitative dynamic model, whereby these molecular interaction events represent the first two stages of a functional feedback loop that closes with the capture of activated Gal4p by newly synthesized Gal3p and Gal80p, decreasing transcriptional activation and creating again the protein complex that can bind incoming galactose molecules. Based on the differential time scales of faster protein interactions versus slower biosynthetic steps, this feedback loop functions as a derivative filter where galactose is the input step signal, and released Gal4p is the output derivative signal. One advantage of such a derivative filter is that GAL genes are expressed in proportion to the cellular requirement. Furthermore, this filter adaptively protects the cellular receptors from saturation by galactose, allowing cells to remain sensitive to variations in galactose concentrations rather than to absolute concentrations. Finally, this feedback loop, by allowing phosphorylation of some active Gal4p, may be essential to initiate the subsequent long-term response. |
1902.01399 | Samuel St-Jean | Samuel St-Jean, Maxime Chamberland, Max A. Viergever, Alexander
Leemans | Reducing variability in along-tract analysis with diffusion profile
realignment | v4: peer-reviewed round 2 v3 : deleted some old text from before
peer-review which was mistakenly included v2 : peer-reviewed version v1:
preprint as submitted to journal NeuroImage | NeuroImage, 2019, ISSN 1053-8119 | 10.1016/j.neuroimage.2019.06.016 | null | q-bio.QM stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction
of the brain's white matter structures through tractography. Analyzing dMRI
measures along the trajectory of white matter bundles can provide a more
specific investigation than considering a region of interest or tract-averaged
measurements. However, performing group analyses with this along-tract strategy
requires correspondence between points of tract pathways across subjects. This
is usually achieved by creating a new common space where the representative
streamlines from every subject are resampled to the same number of points. If
the underlying anatomy of some subjects was altered due to, e.g. disease or
developmental changes, such information might be lost by resampling to a fixed
number of points. In this work, we propose to address the issue of possible
misalignment, which might be present even after resampling, by realigning the
representative streamline of each subject in this 1D space with a new method,
coined diffusion profile realignment (DPR). Experiments on synthetic datasets
show that DPR reduces the coefficient of variation for the mean diffusivity,
fractional anisotropy and apparent fiber density when compared to the unaligned
case. Using 100 in vivo datasets from the HCP, we simulated changes in mean
diffusivity, fractional anisotropy and apparent fiber density. Pairwise
Student's t-tests between these altered subjects and the original subjects
indicate that regional changes are identified after realignment with the DPR
algorithm, while preserving differences previously detected in the unaligned
case. This new correction strategy contributes to revealing effects of interest
which might be hidden by misalignment and has the potential to improve the
specificity in longitudinal population studies beyond the traditional region of
interest based analysis and along-tract analysis workflows.
| [
{
"created": "Mon, 4 Feb 2019 17:45:34 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Mar 2019 15:17:57 GMT",
"version": "v2"
},
{
"created": "Fri, 29 Mar 2019 10:24:05 GMT",
"version": "v3"
},
{
"created": "Wed, 8 May 2019 08:47:05 GMT",
"version": "v4"
}
] | 2019-06-21 | [
[
"St-Jean",
"Samuel",
""
],
[
"Chamberland",
"Maxime",
""
],
[
"Viergever",
"Max A.",
""
],
[
"Leemans",
"Alexander",
""
]
] | Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction of the brain's white matter structures through tractography. Analyzing dMRI measures along the trajectory of white matter bundles can provide a more specific investigation than considering a region of interest or tract-averaged measurements. However, performing group analyses with this along-tract strategy requires correspondence between points of tract pathways across subjects. This is usually achieved by creating a new common space where the representative streamlines from every subject are resampled to the same number of points. If the underlying anatomy of some subjects was altered due to, e.g. disease or developmental changes, such information might be lost by resampling to a fixed number of points. In this work, we propose to address the issue of possible misalignment, which might be present even after resampling, by realigning the representative streamline of each subject in this 1D space with a new method, coined diffusion profile realignment (DPR). Experiments on synthetic datasets show that DPR reduces the coefficient of variation for the mean diffusivity, fractional anisotropy and apparent fiber density when compared to the unaligned case. Using 100 in vivo datasets from the HCP, we simulated changes in mean diffusivity, fractional anisotropy and apparent fiber density. Pairwise Student's t-tests between these altered subjects and the original subjects indicate that regional changes are identified after realignment with the DPR algorithm, while preserving differences previously detected in the unaligned case. This new correction strategy contributes to revealing effects of interest which might be hidden by misalignment and has the potential to improve the specificity in longitudinal population studies beyond the traditional region of interest based analysis and along-tract analysis workflows. |
0709.2646 | Seth Sullivant | Niko Beerenwinkel, Seth Sullivant | Markov models for accumulating mutations | 21 pages, 8 figures | null | null | null | q-bio.PE math.CO | null | We introduce and analyze a waiting time model for the accumulation of genetic
changes. The continuous time conjunctive Bayesian network is defined by a
partially ordered set of mutations and by the rate of fixation of each
mutation. The partial order encodes constraints on the order in which mutations
can fixate in the population, shedding light on the mutational pathways
underlying the evolutionary process. We study a censored version of the model
and derive equations for an EM algorithm to perform maximum likelihood
estimation of the model parameters. We also show how to select the maximum
likelihood poset. The model is applied to genetic data from different cancers
and from drug resistant HIV samples, indicating implications for diagnosis and
treatment.
| [
{
"created": "Mon, 17 Sep 2007 14:25:42 GMT",
"version": "v1"
}
] | 2007-09-18 | [
[
"Beerenwinkel",
"Niko",
""
],
[
"Sullivant",
"Seth",
""
]
] | We introduce and analyze a waiting time model for the accumulation of genetic changes. The continuous time conjunctive Bayesian network is defined by a partially ordered set of mutations and by the rate of fixation of each mutation. The partial order encodes constraints on the order in which mutations can fixate in the population, shedding light on the mutational pathways underlying the evolutionary process. We study a censored version of the model and derive equations for an EM algorithm to perform maximum likelihood estimation of the model parameters. We also show how to select the maximum likelihood poset. The model is applied to genetic data from different cancers and from drug resistant HIV samples, indicating implications for diagnosis and treatment. |
1608.02795 | Guido Tiana | Y. Zhan, L. Giorgetti, G. Tiana | The looping probability of random heteropolymers helps to understand the
scaling properties of biopolymers | null | Phys. Rev. E 94, 032402 (2016) | 10.1103/PhysRevE.94.032402 | null | q-bio.QM cond-mat.soft q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Random heteropolymers are a minimal description of biopolymers and can
provide a theoretical framework to the investigate the formation of loops in
biophysical experiments. A two--state model provides a consistent and robust
way to study the scaling properties of loop formation in polymers of the size
of typical biological systems. Combining it with self--adjusting
simulated--tempering simulations, we can calculate numerically the looping
properties of several realizations of the random interactions within the chain.
Differently from homopolymers, random heteropolymers display at different
temperatures a continuous set of scaling exponents. The necessity of using
self--averaging quantities makes finite--size effects dominant at low
temperatures even for long polymers, shadowing the length--independent
character of looping probability expected in analogy with homopolymeric
globules. This could provide a simple explanation for the small scaling
exponents found in experiments, for example in chromosome folding.
| [
{
"created": "Tue, 9 Aug 2016 13:04:34 GMT",
"version": "v1"
}
] | 2016-09-28 | [
[
"Zhan",
"Y.",
""
],
[
"Giorgetti",
"L.",
""
],
[
"Tiana",
"G.",
""
]
] | Random heteropolymers are a minimal description of biopolymers and can provide a theoretical framework to the investigate the formation of loops in biophysical experiments. A two--state model provides a consistent and robust way to study the scaling properties of loop formation in polymers of the size of typical biological systems. Combining it with self--adjusting simulated--tempering simulations, we can calculate numerically the looping properties of several realizations of the random interactions within the chain. Differently from homopolymers, random heteropolymers display at different temperatures a continuous set of scaling exponents. The necessity of using self--averaging quantities makes finite--size effects dominant at low temperatures even for long polymers, shadowing the length--independent character of looping probability expected in analogy with homopolymeric globules. This could provide a simple explanation for the small scaling exponents found in experiments, for example in chromosome folding. |
1704.02168 | Todd Parsons | Todd L. Parsons | Invasion probabilities, hitting times, and some fluctuation theory for
the stochastic logistic process | null | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider excursions for a class of stochastic processes describing a
population of discrete individuals experiencing density-limited growth, such
that the population has a finite carrying capacity and behaves qualitatively
like the classical logistic model when the carrying capacity is large. Being
discrete and stochastic, however, our population nonetheless goes extinct in
finite time. We present results concerning the maximum of the population prior
to extinction in the large population limit, from which we obtain establishment
probabilities and upper bounds for the process, as well as estimates for the
waiting time to establishment and extinction. As a consequence, we show that
conditional upon establishment, the stochastic logistic process will with high
probability greatly exceed carrying capacity an arbitrary number of times prior
to extinction.
| [
{
"created": "Fri, 7 Apr 2017 10:22:21 GMT",
"version": "v1"
}
] | 2017-04-10 | [
[
"Parsons",
"Todd L.",
""
]
] | We consider excursions for a class of stochastic processes describing a population of discrete individuals experiencing density-limited growth, such that the population has a finite carrying capacity and behaves qualitatively like the classical logistic model when the carrying capacity is large. Being discrete and stochastic, however, our population nonetheless goes extinct in finite time. We present results concerning the maximum of the population prior to extinction in the large population limit, from which we obtain establishment probabilities and upper bounds for the process, as well as estimates for the waiting time to establishment and extinction. As a consequence, we show that conditional upon establishment, the stochastic logistic process will with high probability greatly exceed carrying capacity an arbitrary number of times prior to extinction. |
0902.2918 | Johannes Berg | Franck Stauffer and Johannes Berg | Adaptive gene regulatory networks | 5 pages RevTex | null | 10.1209/0295-5075/88/48004 | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Regulatory interactions between genes show a large amount of cross-species
variability, even when the underlying functions are conserved: There are many
ways to achieve the same function. Here we investigate the ability of
regulatory networks to reproduce given expression levels within a simple model
of gene regulation. We find an exponentially large space of regulatory networks
compatible with a given set of expression levels, giving rise to an extensive
entropy of networks. Typical realisations of regulatory networks are found to
share a bias towards symmetric interactions, in line with empirical evidence.
| [
{
"created": "Tue, 17 Feb 2009 13:02:42 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Stauffer",
"Franck",
""
],
[
"Berg",
"Johannes",
""
]
] | Regulatory interactions between genes show a large amount of cross-species variability, even when the underlying functions are conserved: There are many ways to achieve the same function. Here we investigate the ability of regulatory networks to reproduce given expression levels within a simple model of gene regulation. We find an exponentially large space of regulatory networks compatible with a given set of expression levels, giving rise to an extensive entropy of networks. Typical realisations of regulatory networks are found to share a bias towards symmetric interactions, in line with empirical evidence. |
1510.00576 | Tobias Galla | Julie Eatock, Yen Ting Lin, Eugene T. Y. Chang, Tobias Galla, Richard
H. Clayton | Assessing Measures of Atrial Fibrillation Clustering via Stochastic
Models of Episode Recurrence and Disease Progression | 4 pages, 4 figures, submitted to Computing in Cardiology 2015 | null | null | null | q-bio.TO q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Atrial fibrillation (AF) is a leading cause of morbidity and mortality. AF
prevalence increases with age, which is attributed to pathophysiological
changes that aid AF initiation and perpetuation. Current state-of-the-art
models are only capable of simulating short periods of atrial activity at high
spatial resolution, whilst the majority of clinical recordings are based on
infrequent temporal datasets of limited spatial resolution. Being able to
estimate disease progression informed by both modelling and clinical data would
be of significant interest. In addition an analysis of the temporal
distribution of recorded fibrillation episodes AF density can provide insights
into recurrence patterns. We present an initial analysis of the AF density
measure using a simplified idealised stochastic model of a binary time series
representing AF episodes. The future aim of this work is to develop robust
clinical measures of progression which will be tested on models that generate
long-term synthetic data. These measures would then be of clinical interest in
deciding treatment strategies.
| [
{
"created": "Fri, 2 Oct 2015 12:29:21 GMT",
"version": "v1"
}
] | 2015-10-05 | [
[
"Eatock",
"Julie",
""
],
[
"Lin",
"Yen Ting",
""
],
[
"Chang",
"Eugene T. Y.",
""
],
[
"Galla",
"Tobias",
""
],
[
"Clayton",
"Richard H.",
""
]
] | Atrial fibrillation (AF) is a leading cause of morbidity and mortality. AF prevalence increases with age, which is attributed to pathophysiological changes that aid AF initiation and perpetuation. Current state-of-the-art models are only capable of simulating short periods of atrial activity at high spatial resolution, whilst the majority of clinical recordings are based on infrequent temporal datasets of limited spatial resolution. Being able to estimate disease progression informed by both modelling and clinical data would be of significant interest. In addition an analysis of the temporal distribution of recorded fibrillation episodes AF density can provide insights into recurrence patterns. We present an initial analysis of the AF density measure using a simplified idealised stochastic model of a binary time series representing AF episodes. The future aim of this work is to develop robust clinical measures of progression which will be tested on models that generate long-term synthetic data. These measures would then be of clinical interest in deciding treatment strategies. |
2004.13777 | Vikram Singh | Spandan Kumar, Bhanu Sharma, Vikram Singh | Modelling the role of media induced fear conditioning in mitigating
post-lockdown COVID-19 pandemic: perspectives on India | 21 pages, 8 figures, 1 table | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Several countries that have been successful in constraining the severity of
COVID-19 pandemic via "lockdown" are now considering to slowly end it, mainly
because of enormous socio-economic side-effects. An abrupt ending of lockdown
can increase the basic reproductive number and undo everything; therefore,
carefully designed exit strategies are needed to sustain its benefits post
upliftment. To study the role of fear conditioning on mitigating the spread of
COVID-19 in post-lockdown phase, in this work, we propose an age- and social
contact- structures dependent Susceptible, Feared, Exposed, Infected and
Recovered (SFEIR) model. Simulating the SFEIR model on Indian population with
fear conditioning via mass media (like, television, community radio, internet
and print media) along with positive reinforcement, it is found that increase
in fraction of feared people results in the significant decrease in the growth
of infected population. The present study suggests that, during post-lockdown
phase, media induced fear conditioning in conjunction with closure of schools
for about one more year can serve as an important non-pharmaceutical
intervention to substantially mitigate this pandemic in India. The proposed
SFEIR model, by quantifying the influence of media in inducing fear
conditioning, underlies the importance of community driven changes in country
specific mitigation of COVID-19 spread in post-lockdown phase.
| [
{
"created": "Tue, 28 Apr 2020 19:05:04 GMT",
"version": "v1"
},
{
"created": "Mon, 25 May 2020 15:25:42 GMT",
"version": "v2"
}
] | 2020-05-26 | [
[
"Kumar",
"Spandan",
""
],
[
"Sharma",
"Bhanu",
""
],
[
"Singh",
"Vikram",
""
]
] | Several countries that have been successful in constraining the severity of COVID-19 pandemic via "lockdown" are now considering to slowly end it, mainly because of enormous socio-economic side-effects. An abrupt ending of lockdown can increase the basic reproductive number and undo everything; therefore, carefully designed exit strategies are needed to sustain its benefits post upliftment. To study the role of fear conditioning on mitigating the spread of COVID-19 in post-lockdown phase, in this work, we propose an age- and social contact- structures dependent Susceptible, Feared, Exposed, Infected and Recovered (SFEIR) model. Simulating the SFEIR model on Indian population with fear conditioning via mass media (like, television, community radio, internet and print media) along with positive reinforcement, it is found that increase in fraction of feared people results in the significant decrease in the growth of infected population. The present study suggests that, during post-lockdown phase, media induced fear conditioning in conjunction with closure of schools for about one more year can serve as an important non-pharmaceutical intervention to substantially mitigate this pandemic in India. The proposed SFEIR model, by quantifying the influence of media in inducing fear conditioning, underlies the importance of community driven changes in country specific mitigation of COVID-19 spread in post-lockdown phase. |
2304.01799 | Gavin Mischler | Gavin Mischler, Vinay Raghavan, Menoua Keshishian, Nima Mesgarani | naplib-python: Neural Acoustic Data Processing and Analysis Tools in
Python | 9 pages including references, 1 table, 1 figure | null | 10.1016/j.simpa.2023.100541 | null | q-bio.NC q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recently, the computational neuroscience community has pushed for more
transparent and reproducible methods across the field. In the interest of
unifying the domain of auditory neuroscience, naplib-python provides an
intuitive and general data structure for handling all neural recordings and
stimuli, as well as extensive preprocessing, feature extraction, and analysis
tools which operate on that data structure. The package removes many of the
complications associated with this domain, such as varying trial durations and
multi-modal stimuli, and provides a general-purpose analysis framework that
interfaces easily with existing toolboxes used in the field.
| [
{
"created": "Tue, 4 Apr 2023 13:56:32 GMT",
"version": "v1"
}
] | 2023-09-20 | [
[
"Mischler",
"Gavin",
""
],
[
"Raghavan",
"Vinay",
""
],
[
"Keshishian",
"Menoua",
""
],
[
"Mesgarani",
"Nima",
""
]
] | Recently, the computational neuroscience community has pushed for more transparent and reproducible methods across the field. In the interest of unifying the domain of auditory neuroscience, naplib-python provides an intuitive and general data structure for handling all neural recordings and stimuli, as well as extensive preprocessing, feature extraction, and analysis tools which operate on that data structure. The package removes many of the complications associated with this domain, such as varying trial durations and multi-modal stimuli, and provides a general-purpose analysis framework that interfaces easily with existing toolboxes used in the field. |
2312.01186 | Shuxian Zou | Shuxian Zou, Hui Li, Shentong Mo, Xingyi Cheng, Eric Xing, Le Song | Linker-Tuning: Optimizing Continuous Prompts for Heterodimeric Protein
Prediction | null | null | null | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting the structure of interacting chains is crucial for understanding
biological systems and developing new drugs. Large-scale pre-trained Protein
Language Models (PLMs), such as ESM2, have shown impressive abilities in
extracting biologically meaningful representations for protein structure
prediction. In this paper, we show that ESMFold, which has been successful in
computing accurate atomic structures for single-chain proteins, can be adapted
to predict the heterodimer structures in a lightweight manner. We propose
Linker-tuning, which learns a continuous prompt to connect the two chains in a
dimer before running it as a single sequence in ESMFold. Experiment results
show that our method successfully predicts 56.98% of interfaces on the i.i.d.
heterodimer test set, with an absolute improvement of +12.79% over the
ESMFold-Linker baseline. Furthermore, our model can generalize well to the
out-of-distribution (OOD) test set HeteroTest2 and two antibody test sets Fab
and Fv while being $9\times$ faster than AF-Multimer.
| [
{
"created": "Sat, 2 Dec 2023 17:24:45 GMT",
"version": "v1"
}
] | 2023-12-05 | [
[
"Zou",
"Shuxian",
""
],
[
"Li",
"Hui",
""
],
[
"Mo",
"Shentong",
""
],
[
"Cheng",
"Xingyi",
""
],
[
"Xing",
"Eric",
""
],
[
"Song",
"Le",
""
]
] | Predicting the structure of interacting chains is crucial for understanding biological systems and developing new drugs. Large-scale pre-trained Protein Language Models (PLMs), such as ESM2, have shown impressive abilities in extracting biologically meaningful representations for protein structure prediction. In this paper, we show that ESMFold, which has been successful in computing accurate atomic structures for single-chain proteins, can be adapted to predict the heterodimer structures in a lightweight manner. We propose Linker-tuning, which learns a continuous prompt to connect the two chains in a dimer before running it as a single sequence in ESMFold. Experiment results show that our method successfully predicts 56.98% of interfaces on the i.i.d. heterodimer test set, with an absolute improvement of +12.79% over the ESMFold-Linker baseline. Furthermore, our model can generalize well to the out-of-distribution (OOD) test set HeteroTest2 and two antibody test sets Fab and Fv while being $9\times$ faster than AF-Multimer. |
1505.04518 | Christopher Marriott | Chris Marriott and Jobran Chebib | Emergence-focused design in complex system simulation | European Conference on Artificial Life 2015 - York, UK | null | null | null | q-bio.PE cs.AI cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Emergence is a phenomenon taken for granted in science but also still not
well understood. We have developed a model of artificial genetic evolution
intended to allow for emergence on genetic, population and social levels. We
present the details of the current state of our environment, agent, and
reproductive models. In developing our models we have relied on a principle of
using non-linear systems to model as many systems as possible including
mutation and recombination, gene-environment interaction, agent metabolism,
agent survival, resource gathering and sexual reproduction. In this paper we
review the genetic dynamics that have emerged in our system including
genotype-phenotype divergence, genetic drift, pseudogenes, and gene
duplication. We conclude that emergence-focused design in complex system
simulation is necessary to reproduce the multilevel emergence seen in the
natural world.
| [
{
"created": "Mon, 18 May 2015 05:42:38 GMT",
"version": "v1"
}
] | 2015-05-19 | [
[
"Marriott",
"Chris",
""
],
[
"Chebib",
"Jobran",
""
]
] | Emergence is a phenomenon taken for granted in science but also still not well understood. We have developed a model of artificial genetic evolution intended to allow for emergence on genetic, population and social levels. We present the details of the current state of our environment, agent, and reproductive models. In developing our models we have relied on a principle of using non-linear systems to model as many systems as possible including mutation and recombination, gene-environment interaction, agent metabolism, agent survival, resource gathering and sexual reproduction. In this paper we review the genetic dynamics that have emerged in our system including genotype-phenotype divergence, genetic drift, pseudogenes, and gene duplication. We conclude that emergence-focused design in complex system simulation is necessary to reproduce the multilevel emergence seen in the natural world. |
q-bio/0411015 | Wentian Li | Wentian Li, Dirk Holste | An Unusual 500,000 Bases Long Oscillation of Guanine and Cytosine
Content in Human Chromosome 21 | 15 pages (figures included), 5 figures | Computational Biology and Chemistry, 28(5-6): 393-399 (2004) | 10.1016/j.compbiolchem.2004.09.011 | q-bio.GN/0411015 | q-bio.GN q-bio.QM | null | An oscillation with a period of around 500 kb in guanine and cytosine content
(GC%) is observed in the DNA sequence of human chromosome 21. This oscillation
is localized in the rightmost one-eighth region of the chromosome, from 43.5 Mb
to 46.5 Mb. Five cycles of oscillation are observed in this region with six
GC-rich peaks and five GC-poor valleys. The GC-poor valleys comprise regions
with low density of CpG islands and, alternating between the two DNA strands,
low gene density regions. Consequently, the long-range oscillation of GC%
result in spacing patterns of both CpG island density, and to a lesser extent,
gene densities.
| [
{
"created": "Wed, 3 Nov 2004 20:20:06 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Li",
"Wentian",
""
],
[
"Holste",
"Dirk",
""
]
] | An oscillation with a period of around 500 kb in guanine and cytosine content (GC%) is observed in the DNA sequence of human chromosome 21. This oscillation is localized in the rightmost one-eighth region of the chromosome, from 43.5 Mb to 46.5 Mb. Five cycles of oscillation are observed in this region with six GC-rich peaks and five GC-poor valleys. The GC-poor valleys comprise regions with low density of CpG islands and, alternating between the two DNA strands, low gene density regions. Consequently, the long-range oscillation of GC% result in spacing patterns of both CpG island density, and to a lesser extent, gene densities. |
1412.2779 | Guowei Wei | Kelin Xia and Guo-Wei Wei | Persistent homology analysis of protein structure, flexibility and
folding | 22 figures, 82 references | International Journal for Numerical Methods in Biomedical
Engineering, 30, 814-844 (2014) | 10.1002/cnm.2655 | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proteins are the most important biomolecules for living organisms. The
understanding of protein structure, function, dynamics and transport is one of
most challenging tasks in biological science. In the present work, persistent
homology is, for the first time, introduced for extracting molecular
topological fingerprints (MTFs) based on the persistence of molecular
topological invariants. MTFs are utilized for protein characterization,
identification and classification. The method of slicing is proposed to track
the geometric origin of protein topological invariants. Both all-atom and
coarse-grained representations of MTFs are constructed. A new cutoff-like
filtration is proposed to shed light on the optimal cutoff distance in elastic
network models. Based on the correlation between protein compactness, rigidity
and connectivity, we propose an accumulated bar length generated from
persistent topological invariants for the quantitative modeling of protein
flexibility. To this end, a correlation matrix based filtration is developed.
This approach gives rise to an accurate prediction of the optimal
characteristic distance used in protein B-factor analysis. Finally, MTFs are
employed to characterize protein topological evolution during protein folding
and quantitatively predict the protein folding stability. An excellent
consistence between our persistent homology prediction and molecular dynamics
simulation is found. This work reveals the topology-function relationship of
proteins.
| [
{
"created": "Mon, 8 Dec 2014 21:24:50 GMT",
"version": "v1"
}
] | 2014-12-10 | [
[
"Xia",
"Kelin",
""
],
[
"Wei",
"Guo-Wei",
""
]
] | Proteins are the most important biomolecules for living organisms. The understanding of protein structure, function, dynamics and transport is one of most challenging tasks in biological science. In the present work, persistent homology is, for the first time, introduced for extracting molecular topological fingerprints (MTFs) based on the persistence of molecular topological invariants. MTFs are utilized for protein characterization, identification and classification. The method of slicing is proposed to track the geometric origin of protein topological invariants. Both all-atom and coarse-grained representations of MTFs are constructed. A new cutoff-like filtration is proposed to shed light on the optimal cutoff distance in elastic network models. Based on the correlation between protein compactness, rigidity and connectivity, we propose an accumulated bar length generated from persistent topological invariants for the quantitative modeling of protein flexibility. To this end, a correlation matrix based filtration is developed. This approach gives rise to an accurate prediction of the optimal characteristic distance used in protein B-factor analysis. Finally, MTFs are employed to characterize protein topological evolution during protein folding and quantitatively predict the protein folding stability. An excellent consistence between our persistent homology prediction and molecular dynamics simulation is found. This work reveals the topology-function relationship of proteins. |
0902.2970 | Stephen Willson | Stephen J. Willson | Regular networks are determined by their trees | 16 pages | IEEE/ACM Transactions on Computational Biology and Bioinformatics
8 (2011) 785-796 | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A rooted acyclic digraph N with labelled leaves displays a tree T when there
exists a way to select a unique parent of each hybrid vertex resulting in the
tree T. Let Tr(N) denote the set of all trees displayed by the network N. In
general, there may be many other networks M such that Tr(M) = Tr(N). A network
is regular if it is isomorphic with its cover digraph. This paper shows that if
N is regular, there is a procedure to reconstruct N given Tr(N). Hence if N and
M are regular networks and Tr(N) = Tr(M), it follows that N = M, proving that a
regular network is uniquely determined by its displayed trees.
| [
{
"created": "Tue, 17 Feb 2009 18:56:10 GMT",
"version": "v1"
}
] | 2015-01-30 | [
[
"Willson",
"Stephen J.",
""
]
] | A rooted acyclic digraph N with labelled leaves displays a tree T when there exists a way to select a unique parent of each hybrid vertex resulting in the tree T. Let Tr(N) denote the set of all trees displayed by the network N. In general, there may be many other networks M such that Tr(M) = Tr(N). A network is regular if it is isomorphic with its cover digraph. This paper shows that if N is regular, there is a procedure to reconstruct N given Tr(N). Hence if N and M are regular networks and Tr(N) = Tr(M), it follows that N = M, proving that a regular network is uniquely determined by its displayed trees. |
q-bio/0609044 | Jose Vilar | Leonor Saiz and Jose M. G. Vilar | Stochastic dynamics of macromolecular-assembly networks | Open Access article available at
http://www.nature.com/msb/journal/v2/n1/full/msb4100061.html | Nature/EMBO Molecular Systems Biology 2, art. no. msb4100061, pp.
2006.0024 (2006) | 10.1038/msb4100061 | null | q-bio.MN cond-mat.soft physics.bio-ph q-bio.SC | null | The formation and regulation of macromolecular complexes provides the
backbone of most cellular processes, including gene regulation and signal
transduction. The inherent complexity of assembling macromolecular structures
makes current computational methods strongly limited for understanding how the
physical interactions between cellular components give rise to systemic
properties of cells. Here we present a stochastic approach to study the
dynamics of networks formed by macromolecular complexes in terms of the
molecular interactions of their components. Exploiting key thermodynamic
concepts, this approach makes it possible to both estimate reaction rates and
incorporate the resulting assembly dynamics into the stochastic kinetics of
cellular networks. As prototype systems, we consider the lac operon and phage
lambda induction switches, which rely on the formation of DNA loops by proteins
and on the integration of these protein-DNA complexes into intracellular
networks. This cross-scale approach offers an effective starting point to move
forward from network diagrams, such as those of protein-protein and DNA-protein
interaction networks, to the actual dynamics of cellular processes.
| [
{
"created": "Tue, 26 Sep 2006 16:28:27 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Sep 2006 20:47:00 GMT",
"version": "v2"
}
] | 2007-05-23 | [
[
"Saiz",
"Leonor",
""
],
[
"Vilar",
"Jose M. G.",
""
]
] | The formation and regulation of macromolecular complexes provides the backbone of most cellular processes, including gene regulation and signal transduction. The inherent complexity of assembling macromolecular structures makes current computational methods strongly limited for understanding how the physical interactions between cellular components give rise to systemic properties of cells. Here we present a stochastic approach to study the dynamics of networks formed by macromolecular complexes in terms of the molecular interactions of their components. Exploiting key thermodynamic concepts, this approach makes it possible to both estimate reaction rates and incorporate the resulting assembly dynamics into the stochastic kinetics of cellular networks. As prototype systems, we consider the lac operon and phage lambda induction switches, which rely on the formation of DNA loops by proteins and on the integration of these protein-DNA complexes into intracellular networks. This cross-scale approach offers an effective starting point to move forward from network diagrams, such as those of protein-protein and DNA-protein interaction networks, to the actual dynamics of cellular processes. |
1708.03765 | Romulus Breban | Pavel Polyakov, C\'ecile Souty, Pierre-Yves B\"oelle and Romulus
Breban | Spatial heterogeneity analyses identify limitations of epidemic alert
systems: Monitoring influenza-like illness in France | 24 pages, 1 table, 4 figures | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Surveillance data serving for epidemic alert systems are typically fully
aggregated in space. However, epidemics may be spatially heterogeneous,
undergoing distinct dynamics in distinct regions of the surveillance area. We
unveil this in retrospective analyses by classifying incidence time series. We
use Pearson correlation to quantify the similarity between local time series
and then classify them using modularity maximization. The surveillance area is
thus divided into regions with different incidence patterns. We analyzed 31
years of data on influenza-like-illness from the French system Sentinelles and
found spatial heterogeneity in 19/31 influenza seasons. However, distinct
epidemic regions could be identified only 4-5 weeks after the nationwide alert.
The impact of spatial heterogeneity on influenza epidemiology was complex.
First, when the nationwide alert was triggered, 32-41% of the administrative
regions were experiencing an epidemic, while the others were not. Second, the
nationwide alert was timely for the whole surveillance area, but, subsequently,
regions experienced distinct epidemic dynamics. Third, the epidemic dynamics
were homogeneous in space. Spatial heterogeneity analyses can provide the
timing of the epidemic peak and finish, in various regions, to tailor disease
monitoring and control.
| [
{
"created": "Sat, 12 Aug 2017 11:13:14 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Mar 2018 08:01:49 GMT",
"version": "v2"
}
] | 2018-03-30 | [
[
"Polyakov",
"Pavel",
""
],
[
"Souty",
"Cécile",
""
],
[
"Böelle",
"Pierre-Yves",
""
],
[
"Breban",
"Romulus",
""
]
] | Surveillance data serving for epidemic alert systems are typically fully aggregated in space. However, epidemics may be spatially heterogeneous, undergoing distinct dynamics in distinct regions of the surveillance area. We unveil this in retrospective analyses by classifying incidence time series. We use Pearson correlation to quantify the similarity between local time series and then classify them using modularity maximization. The surveillance area is thus divided into regions with different incidence patterns. We analyzed 31 years of data on influenza-like-illness from the French system Sentinelles and found spatial heterogeneity in 19/31 influenza seasons. However, distinct epidemic regions could be identified only 4-5 weeks after the nationwide alert. The impact of spatial heterogeneity on influenza epidemiology was complex. First, when the nationwide alert was triggered, 32-41% of the administrative regions were experiencing an epidemic, while the others were not. Second, the nationwide alert was timely for the whole surveillance area, but, subsequently, regions experienced distinct epidemic dynamics. Third, the epidemic dynamics were homogeneous in space. Spatial heterogeneity analyses can provide the timing of the epidemic peak and finish, in various regions, to tailor disease monitoring and control. |
1204.0997 | Gautier Stoll | Gautier Stoll, Eric Viara, Emmanuel Barillot, Laurence Calzone | Continuous time Boolean modeling for biological signaling: application
of Gillespie algorithm | 10 pages, 9 figures | null | null | null | q-bio.MN physics.bio-ph q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article presents an algorithm that allows modeling of biological
networks in a qualitative framework with continuous time. Mathematical modeling
is used as a systems biology tool to answer biological questions, and more
precisely, to validate a network that describes biological observations and to
predict the effect of perturbations.
We propose a modeling approach that is intrinsically continuous in time. The
algorithm presented here fills the gap between qualitative and quantitative
modeling. It is based on continuous time Markov process applied on a Boolean
state space. In order to describe the temporal evolution, we explicitly specify
the transition rates for each node. For that purpose, we built a language that
can be seen as a generalization of Boolean equations. The values of transition
rates have a natural interpretation: it is the inverse of the time for the
transition to occur. Mathematically, this approach can be translated in a set
of ordinary differential equations on probability distributions; therefore, it
can be seen as an approach in between quantitative and qualitative.
We developed a C++ software, MaBoSS, that is able to simulate such a system
by applying Kinetic Monte-Carlo (or Gillespie algorithm) in the Boolean state
space. This software, parallelized and optimized, computes temporal evolution
of probability distributions and can also estimate stationary distributions.
Applications of Boolean Kinetic Monte-Carlo have been demonstrated for two
qualitative models: a toy model and a published p53/Mdm2 model. Our approach
allows to describe kinetic phenomena which were difficult to handle in the
original models. In particular, transient effects are represented by time
dependent probability distributions, interpretable in terms of cell
populations.
| [
{
"created": "Wed, 4 Apr 2012 16:47:33 GMT",
"version": "v1"
},
{
"created": "Tue, 29 May 2012 13:51:13 GMT",
"version": "v2"
}
] | 2012-05-30 | [
[
"Stoll",
"Gautier",
""
],
[
"Viara",
"Eric",
""
],
[
"Barillot",
"Emmanuel",
""
],
[
"Calzone",
"Laurence",
""
]
] | This article presents an algorithm that allows modeling of biological networks in a qualitative framework with continuous time. Mathematical modeling is used as a systems biology tool to answer biological questions, and more precisely, to validate a network that describes biological observations and to predict the effect of perturbations. We propose a modeling approach that is intrinsically continuous in time. The algorithm presented here fills the gap between qualitative and quantitative modeling. It is based on continuous time Markov process applied on a Boolean state space. In order to describe the temporal evolution, we explicitly specify the transition rates for each node. For that purpose, we built a language that can be seen as a generalization of Boolean equations. The values of transition rates have a natural interpretation: it is the inverse of the time for the transition to occur. Mathematically, this approach can be translated in a set of ordinary differential equations on probability distributions; therefore, it can be seen as an approach in between quantitative and qualitative. We developed a C++ software, MaBoSS, that is able to simulate such a system by applying Kinetic Monte-Carlo (or Gillespie algorithm) in the Boolean state space. This software, parallelized and optimized, computes temporal evolution of probability distributions and can also estimate stationary distributions. Applications of Boolean Kinetic Monte-Carlo have been demonstrated for two qualitative models: a toy model and a published p53/Mdm2 model. Our approach allows to describe kinetic phenomena which were difficult to handle in the original models. In particular, transient effects are represented by time dependent probability distributions, interpretable in terms of cell populations. |
2407.10832 | Alexandria Volkening | Alexandria Volkening | Methods for quantifying self-organization in biology: a forward-looking
survey and tutorial | Tutorial survey on methods for quantifying biological patterns | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | From flocking birds to schooling fish, organisms interact to form collective
dynamics across the natural world. Self-organization is present at smaller
scales as well: cells interact and move during development to produce patterns
in fish skin, and wound healing relies on cell migration. Across these
examples, scientists are interested in shedding light on the individual
behaviors informing spatial group dynamics and in predicting the patterns that
will emerge under altered agent interactions. One challenge to these goals is
that images of self-organization -- whether empirical or generated by models --
are qualitative. To get around this, there are many methods for transforming
qualitative pattern data into quantitative information. In this tutorial
chapter, I survey some methods for quantifying self-organization, including
order parameters, pair correlation functions, and techniques from topological
data analysis. I also discuss some places that I see as especially promising
for quantitative data, modeling, and data-driven approaches to continue meeting
in the future.
| [
{
"created": "Mon, 15 Jul 2024 15:43:16 GMT",
"version": "v1"
}
] | 2024-07-16 | [
[
"Volkening",
"Alexandria",
""
]
] | From flocking birds to schooling fish, organisms interact to form collective dynamics across the natural world. Self-organization is present at smaller scales as well: cells interact and move during development to produce patterns in fish skin, and wound healing relies on cell migration. Across these examples, scientists are interested in shedding light on the individual behaviors informing spatial group dynamics and in predicting the patterns that will emerge under altered agent interactions. One challenge to these goals is that images of self-organization -- whether empirical or generated by models -- are qualitative. To get around this, there are many methods for transforming qualitative pattern data into quantitative information. In this tutorial chapter, I survey some methods for quantifying self-organization, including order parameters, pair correlation functions, and techniques from topological data analysis. I also discuss some places that I see as especially promising for quantitative data, modeling, and data-driven approaches to continue meeting in the future. |
2304.12693 | Neil Scheidwasser | Matthew J Penn, Neil Scheidwasser, Mark P Khurana, David A Duch\^ene,
Christl A Donnelly, Samir Bhatt | Phylo2Vec: a vector representation for binary trees | 36 pages, 9 figures, 1 table, 2 supplementary figures | null | null | null | q-bio.PE cs.LG q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Binary phylogenetic trees inferred from biological data are central to
understanding the shared history among evolutionary units. However, inferring
the placement of latent nodes in a tree is NP-hard and thus computationally
expensive. State-of-the-art methods rely on carefully designed heuristics for
tree search. These methods use different data structures for easy manipulation
(e.g., classes in object-oriented programming languages) and readable
representation of trees (e.g., Newick-format strings). Here, we present
Phylo2Vec, a parsimonious encoding for phylogenetic trees that serves as a
unified approach for both manipulating and representing phylogenetic trees.
Phylo2Vec maps any binary tree with $n$ leaves to a unique integer vector of
length $n-1$. The advantages of Phylo2Vec are fourfold: i) fast tree sampling,
(ii) compressed tree representation compared to a Newick string, iii) quick and
unambiguous verification if two binary trees are identical topologically, and
iv) systematic ability to traverse tree space in very large or small jumps. As
a proof of concept, we use Phylo2Vec for maximum likelihood inference on five
real-world datasets and show that a simple hill-climbing-based optimisation
scheme can efficiently traverse the vastness of tree space from a random to an
optimal tree.
| [
{
"created": "Tue, 25 Apr 2023 09:54:35 GMT",
"version": "v1"
},
{
"created": "Fri, 1 Dec 2023 08:26:28 GMT",
"version": "v2"
},
{
"created": "Fri, 10 May 2024 14:31:10 GMT",
"version": "v3"
}
] | 2024-05-13 | [
[
"Penn",
"Matthew J",
""
],
[
"Scheidwasser",
"Neil",
""
],
[
"Khurana",
"Mark P",
""
],
[
"Duchêne",
"David A",
""
],
[
"Donnelly",
"Christl A",
""
],
[
"Bhatt",
"Samir",
""
]
] | Binary phylogenetic trees inferred from biological data are central to understanding the shared history among evolutionary units. However, inferring the placement of latent nodes in a tree is NP-hard and thus computationally expensive. State-of-the-art methods rely on carefully designed heuristics for tree search. These methods use different data structures for easy manipulation (e.g., classes in object-oriented programming languages) and readable representation of trees (e.g., Newick-format strings). Here, we present Phylo2Vec, a parsimonious encoding for phylogenetic trees that serves as a unified approach for both manipulating and representing phylogenetic trees. Phylo2Vec maps any binary tree with $n$ leaves to a unique integer vector of length $n-1$. The advantages of Phylo2Vec are fourfold: i) fast tree sampling, (ii) compressed tree representation compared to a Newick string, iii) quick and unambiguous verification if two binary trees are identical topologically, and iv) systematic ability to traverse tree space in very large or small jumps. As a proof of concept, we use Phylo2Vec for maximum likelihood inference on five real-world datasets and show that a simple hill-climbing-based optimisation scheme can efficiently traverse the vastness of tree space from a random to an optimal tree. |
2007.09800 | Joaquin Salas | Joaqu\'in Salas | Improving the Estimation of the COVID-19 Effective Reproduction Number
using Nowcasting | 11 pages, 5 figures | null | null | null | q-bio.PE physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As the interactions between people increases, the impending menace of
COVID-19 outbreaks materialize, and there is an inclination to apply lockdowns.
In this context, it is essential to have easy-to-use indicators for people to
use as a reference. The basic reproduction number of confirmed positives,
$R_t$, fulfill such a role. This document proposes a data-driven approach to
nowcast $R_t$ based on previous observations' statistical behavior. As more
information arrives, the method naturally becomes more precise about the final
count of confirmed positives. Our method's strength is that it is based on the
self-reported onset of symptoms, in contrast to other methods that use the
daily report's count to infer this quantity. We show that our approach may be
the foundation for determining useful epidemy tracking indicators.
| [
{
"created": "Sun, 19 Jul 2020 22:17:26 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Jan 2021 05:57:54 GMT",
"version": "v2"
}
] | 2021-01-26 | [
[
"Salas",
"Joaquín",
""
]
] | As the interactions between people increases, the impending menace of COVID-19 outbreaks materialize, and there is an inclination to apply lockdowns. In this context, it is essential to have easy-to-use indicators for people to use as a reference. The basic reproduction number of confirmed positives, $R_t$, fulfill such a role. This document proposes a data-driven approach to nowcast $R_t$ based on previous observations' statistical behavior. As more information arrives, the method naturally becomes more precise about the final count of confirmed positives. Our method's strength is that it is based on the self-reported onset of symptoms, in contrast to other methods that use the daily report's count to infer this quantity. We show that our approach may be the foundation for determining useful epidemy tracking indicators. |
1709.05429 | Hector Zenil | Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng, Szabolcs
Elias, Angelika Schmidt, Gordon Ball, Jesper Tegn\'er | An Algorithmic Information Calculus for Causal Discovery and
Reprogramming Systems | 50 pages with Supplementary Information and Extended Figures. The
Online Algorithmic Complexity Calculator implements the methods in this
paper: http://complexitycalculator.com/ Animated video available at:
https://youtu.be/ufzq2p5tVLI | null | null | null | q-bio.OT cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We demonstrate that the algorithmic information content of a system is deeply
connected to its potential dynamics, thus affording an avenue for moving
systems in the information-theoretic space and controlling them in the phase
space. To this end we performed experiments and validated the results on (1) a
very large set of small graphs, (2) a number of larger networks with different
topologies, and (3) biological networks from a widely studied and validated
genetic network (e.coli) as well as on a significant number of differentiating
(Th17) and differentiated human cells from high quality databases (Harvard's
CellNet) with results conforming to experimentally validated biological data.
Based on these results we introduce a conceptual framework, a model-based
interventional calculus and a reprogrammability measure with which to steer,
manipulate, and reconstruct the dynamics of non- linear dynamical systems from
partial and disordered observations. The method consists in finding and
applying a series of controlled interventions to a dynamical system to estimate
how its algorithmic information content is affected when every one of its
elements are perturbed. The approach represents an alternative to numerical
simulation and statistical approaches for inferring causal
mechanistic/generative models and finding first principles. We demonstrate the
framework's capabilities by reconstructing the phase space of some discrete
dynamical systems (cellular automata) as case study and reconstructing their
generating rules. We thus advance tools for reprogramming artificial and living
systems without full knowledge or access to the system's actual kinetic
equations or probability distributions yielding a suite of universal and
parameter-free algorithms of wide applicability ranging from causation,
dimension reduction, feature selection and model generation.
| [
{
"created": "Fri, 15 Sep 2017 22:41:38 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Mar 2018 22:48:09 GMT",
"version": "v10"
},
{
"created": "Thu, 5 Apr 2018 15:38:29 GMT",
"version": "v11"
},
{
"created": "Tue, 19 Sep 2017 01:01:24 GMT",
"version": "v2"
},
{
"... | 2018-04-06 | [
[
"Zenil",
"Hector",
""
],
[
"Kiani",
"Narsis A.",
""
],
[
"Marabita",
"Francesco",
""
],
[
"Deng",
"Yue",
""
],
[
"Elias",
"Szabolcs",
""
],
[
"Schmidt",
"Angelika",
""
],
[
"Ball",
"Gordon",
""
],
[
... | We demonstrate that the algorithmic information content of a system is deeply connected to its potential dynamics, thus affording an avenue for moving systems in the information-theoretic space and controlling them in the phase space. To this end we performed experiments and validated the results on (1) a very large set of small graphs, (2) a number of larger networks with different topologies, and (3) biological networks from a widely studied and validated genetic network (e.coli) as well as on a significant number of differentiating (Th17) and differentiated human cells from high quality databases (Harvard's CellNet) with results conforming to experimentally validated biological data. Based on these results we introduce a conceptual framework, a model-based interventional calculus and a reprogrammability measure with which to steer, manipulate, and reconstruct the dynamics of non- linear dynamical systems from partial and disordered observations. The method consists in finding and applying a series of controlled interventions to a dynamical system to estimate how its algorithmic information content is affected when every one of its elements are perturbed. The approach represents an alternative to numerical simulation and statistical approaches for inferring causal mechanistic/generative models and finding first principles. We demonstrate the framework's capabilities by reconstructing the phase space of some discrete dynamical systems (cellular automata) as case study and reconstructing their generating rules. We thus advance tools for reprogramming artificial and living systems without full knowledge or access to the system's actual kinetic equations or probability distributions yielding a suite of universal and parameter-free algorithms of wide applicability ranging from causation, dimension reduction, feature selection and model generation. |
1603.05897 | Manlio De Domenico | Manlio De Domenico, Shuntaro Sasai and Alex Arenas | Mapping multiplex hubs in human functional brain network | 11 pages, 8 figures, 2 tables | Front. Neurosci. 10, 326 (2016) | 10.3389/fnins.2016.00326 | null | q-bio.NC cond-mat.dis-nn physics.bio-ph physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Typical brain networks consist of many peripheral regions and a few highly
central ones, i.e. hubs, playing key functional roles in cerebral
inter-regional interactions. Studies have shown that networks, obtained from
the analysis of specific frequency components of brain activity, present
peculiar architectures with unique profiles of region centrality. However, the
identification of hubs in networks built from different frequency bands
simultaneously is still a challenging problem, remaining largely unexplored.
Here we identify each frequency component with one layer of a multiplex network
and face this challenge by exploiting the recent advances in the analysis of
multiplex topologies. First, we show that each frequency band carries unique
topological information, fundamental to accurately model brain functional
networks. We then demonstrate that hubs in the multiplex network, in general
different from those ones obtained after discarding or aggregating the measured
signals as usual, provide a more accurate map of brain's most important
functional regions, allowing to distinguish between healthy and schizophrenic
populations better than conventional network approaches.
| [
{
"created": "Fri, 18 Mar 2016 15:53:07 GMT",
"version": "v1"
}
] | 2016-07-19 | [
[
"De Domenico",
"Manlio",
""
],
[
"Sasai",
"Shuntaro",
""
],
[
"Arenas",
"Alex",
""
]
] | Typical brain networks consist of many peripheral regions and a few highly central ones, i.e. hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches. |
2208.00530 | Nikolai Slavov | Michael J. MacCoss, Javier Alfaro, Meni Wanunu, Danielle A. Faivre,
and Nikolai Slavov | Sampling the proteome by emerging single-molecule and mass-spectrometry
methods | Recorded presentation: https://youtu.be/w0IOgJrrvNM | Nat Methods 20, 339--346 (2023) | 10.1038/s41592-023-01802-5 | null | q-bio.QM | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Mammalian cells have about 30,000-fold more protein molecules than mRNA
molecules. This larger number of molecules and the associated larger dynamic
range have major implications in the development of proteomics technologies. We
examine these implications for both liquid chromatography-tandem mass
spectrometry (LC-MS/MS) and single-molecule counting and provide estimates on
how many molecules are routinely measured in proteomics experiments by
LC-MS/MS. We review strategies that have been helpful for counting billions of
protein molecules by LC-MS/MS and suggest that these strategies can benefit
single-molecule methods, especially in mitigating the challenges of the wide
dynamic range of the proteome. We also examine the theoretical possibilities
for scaling up single-molecule and mass spectrometry proteomics approaches to
quantifying the billions of protein molecules that make up the proteomes of our
cells.
| [
{
"created": "Sun, 31 Jul 2022 21:59:40 GMT",
"version": "v1"
},
{
"created": "Fri, 27 Jan 2023 21:52:08 GMT",
"version": "v2"
}
] | 2023-03-14 | [
[
"MacCoss",
"Michael J.",
""
],
[
"Alfaro",
"Javier",
""
],
[
"Wanunu",
"Meni",
""
],
[
"Faivre",
"Danielle A.",
""
],
[
"Slavov",
"Nikolai",
""
]
] | Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules. This larger number of molecules and the associated larger dynamic range have major implications in the development of proteomics technologies. We examine these implications for both liquid chromatography-tandem mass spectrometry (LC-MS/MS) and single-molecule counting and provide estimates on how many molecules are routinely measured in proteomics experiments by LC-MS/MS. We review strategies that have been helpful for counting billions of protein molecules by LC-MS/MS and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome. We also examine the theoretical possibilities for scaling up single-molecule and mass spectrometry proteomics approaches to quantifying the billions of protein molecules that make up the proteomes of our cells. |
2311.03131 | Ilya Auslender | Ilya Auslender, Giorgio Letti, Yasaman Heydari, Clara Zaccaria,
Lorenzo Pavesi | Decoding Neuronal Networks: A Reservoir Computing Approach for
Predicting Connectivity and Functionality | Submitted version | null | null | null | q-bio.QM cs.LG physics.bio-ph | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In this study, we address the challenge of analyzing electrophysiological
measurements in neuronal networks. Our computational model, based on the
Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data
obtained from electrophysiological measurements of neuronal cultures. By
reconstructing the network structure on a macroscopic scale, we reveal the
connectivity between neuronal units. Notably, our model outperforms common
methods like Cross-Correlation and Transfer-Entropy in predicting the network's
connectivity map. Furthermore, we experimentally validate its ability to
forecast network responses to specific inputs, including localized optogenetic
stimuli.
| [
{
"created": "Mon, 6 Nov 2023 14:28:11 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Jan 2024 17:29:54 GMT",
"version": "v2"
},
{
"created": "Tue, 5 Mar 2024 10:25:03 GMT",
"version": "v3"
}
] | 2024-03-06 | [
[
"Auslender",
"Ilya",
""
],
[
"Letti",
"Giorgio",
""
],
[
"Heydari",
"Yasaman",
""
],
[
"Zaccaria",
"Clara",
""
],
[
"Pavesi",
"Lorenzo",
""
]
] | In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophysiological measurements of neuronal cultures. By reconstructing the network structure on a macroscopic scale, we reveal the connectivity between neuronal units. Notably, our model outperforms common methods like Cross-Correlation and Transfer-Entropy in predicting the network's connectivity map. Furthermore, we experimentally validate its ability to forecast network responses to specific inputs, including localized optogenetic stimuli. |
1901.06794 | Chunmei Feng | Jin-Xing Liu, Chun-Mei Feng, Xiang-Zhen Kong, Yong Xu | Dual Graph-Laplacian PCA: A Closed-Form Solution for Bi-clustering to
Find "Checkerboard" Structures on Gene Expression Data | This manuscript was submitted in IEEE Transaction on Knowledge and
Data Engineering on 12/01/2017. 9 pages, 3 figures | null | null | null | q-bio.GN cs.CE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the context of cancer, internal "checkerboard" structures are normally
found in the matrices of gene expression data, which correspond to genes that
are significantly up- or down-regulated in patients with specific types of
tumors. In this paper, we propose a novel method, called dual
graph-regularization principal component analysis (DGPCA). The main innovation
of this method is that it simultaneously considers the internal geometric
structures of the condition manifold and the gene manifold. Specifically, we
obtain principal components (PCs) to represent the data and approximate the
cluster membership indicators through Laplacian embedding. This new method is
endowed with internal geometric structures, such as the condition manifold and
gene manifold, which are both suitable for bi-clustering. A closed-form
solution is provided for DGPCA. We apply this new method to simultaneously
cluster genes and conditions (e.g., different samples) with the aim of finding
internal "checkerboard" structures on gene expression data, if they exist.
Then, we use this new method to identify regulatory genes under the particular
conditions and to compare the results with those of other state-of-the-art
PCA-based methods. Promising results on gene expression data have been verified
by extensive experiments
| [
{
"created": "Mon, 21 Jan 2019 05:43:31 GMT",
"version": "v1"
}
] | 2019-01-23 | [
[
"Liu",
"Jin-Xing",
""
],
[
"Feng",
"Chun-Mei",
""
],
[
"Kong",
"Xiang-Zhen",
""
],
[
"Xu",
"Yong",
""
]
] | In the context of cancer, internal "checkerboard" structures are normally found in the matrices of gene expression data, which correspond to genes that are significantly up- or down-regulated in patients with specific types of tumors. In this paper, we propose a novel method, called dual graph-regularization principal component analysis (DGPCA). The main innovation of this method is that it simultaneously considers the internal geometric structures of the condition manifold and the gene manifold. Specifically, we obtain principal components (PCs) to represent the data and approximate the cluster membership indicators through Laplacian embedding. This new method is endowed with internal geometric structures, such as the condition manifold and gene manifold, which are both suitable for bi-clustering. A closed-form solution is provided for DGPCA. We apply this new method to simultaneously cluster genes and conditions (e.g., different samples) with the aim of finding internal "checkerboard" structures on gene expression data, if they exist. Then, we use this new method to identify regulatory genes under the particular conditions and to compare the results with those of other state-of-the-art PCA-based methods. Promising results on gene expression data have been verified by extensive experiments |
1705.06911 | Taikai Takeda | Taikai Takeda, Michiaki Hamada | Beyond similarity assessment: Selecting the optimal model for sequence
alignment via the Factorized Asymptotic Bayesian algorithm | This article has been accepted for publication in Bioinformatics
Published by Oxford University Press | Bioinformatics, 2017, btx643 | 10.1093/bioinformatics/btx643 | null | q-bio.QM stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise
sequence alignment, a quintessential problem in bioinformatics. PHMMs include
three types of hidden states: match, insertion and deletion. Most previous
studies have used one or two hidden states for each PHMM state type. However,
few studies have examined the number of states suitable for representing
sequence data or improving alignment accuracy.We developed a novel method to
select superior models (including the number of hidden states) for PHMM. Our
method selects models with the highest posterior probability using Factorized
Information Criteria (FIC), which is widely utilised in model selection for
probabilistic models with hidden variables. Our simulations indicated this
method has excellent model selection capabilities with slightly improved
alignment accuracy. We applied our method to DNA datasets from 5 and 28
species, ultimately selecting more complex models than those used in previous
studies.
| [
{
"created": "Fri, 19 May 2017 09:49:59 GMT",
"version": "v1"
},
{
"created": "Sun, 15 Oct 2017 06:52:17 GMT",
"version": "v2"
}
] | 2017-10-17 | [
[
"Takeda",
"Taikai",
""
],
[
"Hamada",
"Michiaki",
""
]
] | Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise sequence alignment, a quintessential problem in bioinformatics. PHMMs include three types of hidden states: match, insertion and deletion. Most previous studies have used one or two hidden states for each PHMM state type. However, few studies have examined the number of states suitable for representing sequence data or improving alignment accuracy.We developed a novel method to select superior models (including the number of hidden states) for PHMM. Our method selects models with the highest posterior probability using Factorized Information Criteria (FIC), which is widely utilised in model selection for probabilistic models with hidden variables. Our simulations indicated this method has excellent model selection capabilities with slightly improved alignment accuracy. We applied our method to DNA datasets from 5 and 28 species, ultimately selecting more complex models than those used in previous studies. |
1305.6231 | Kristina Crona | Kristina Crona, Devin Greene, Miriam Barlow | Evolutionary Predictability and Complications with Additivity | null | null | null | null | q-bio.PE q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Adaptation is a central topic in theoretical biology, of practical importance
for analyzing drug resistance mutations. Several authors have used arguments
based on extreme value theory in their work on adaptation. There are
complications with these approaches if fitness is additive (meaning that
fitness effects of mutations sum), or whenever there is more additivity than
what one would expect in an uncorrelated fitness landscape. However, the
approaches have been used in published work, even in situations with
substantial amounts of additivity. In particular, extreme value theory has been
used in discussions on evolutionary predictability. We say that evolution is
predictable if the use of a particular drug at different locations tends lead
to the same resistance mutations. Evolutionary predictability depends on the
probabilities of mutational trajectories. Arguments about probabilities based
on extreme value theory can be misleading. Additivity may cause errors in
estimates of the probabilities of some mutational trajectories by a factor 20
even for rather small examples. We show that additivity gives systematic errors
so as to exaggerate the differences between the most and the least likely
trajectory. As a result of this bias, evolution may appear more predictable
than it is. From a broader perspective, our results suggest that approaches
which depend on the Orr-Gillespie theory are likely to give misleading results
for realistic fitness landscapes whenever one considers adaptation in several
steps.
| [
{
"created": "Mon, 27 May 2013 14:17:41 GMT",
"version": "v1"
},
{
"created": "Sat, 14 Dec 2013 19:26:53 GMT",
"version": "v2"
}
] | 2013-12-17 | [
[
"Crona",
"Kristina",
""
],
[
"Greene",
"Devin",
""
],
[
"Barlow",
"Miriam",
""
]
] | Adaptation is a central topic in theoretical biology, of practical importance for analyzing drug resistance mutations. Several authors have used arguments based on extreme value theory in their work on adaptation. There are complications with these approaches if fitness is additive (meaning that fitness effects of mutations sum), or whenever there is more additivity than what one would expect in an uncorrelated fitness landscape. However, the approaches have been used in published work, even in situations with substantial amounts of additivity. In particular, extreme value theory has been used in discussions on evolutionary predictability. We say that evolution is predictable if the use of a particular drug at different locations tends lead to the same resistance mutations. Evolutionary predictability depends on the probabilities of mutational trajectories. Arguments about probabilities based on extreme value theory can be misleading. Additivity may cause errors in estimates of the probabilities of some mutational trajectories by a factor 20 even for rather small examples. We show that additivity gives systematic errors so as to exaggerate the differences between the most and the least likely trajectory. As a result of this bias, evolution may appear more predictable than it is. From a broader perspective, our results suggest that approaches which depend on the Orr-Gillespie theory are likely to give misleading results for realistic fitness landscapes whenever one considers adaptation in several steps. |
2104.05057 | Etienne Racine | Etienne Racine, Nicholas C. Coops, Jean B\'egin, Mari Myllym\"aki | Tree species, crown cover, and age as determinants of the vertical
distribution of airborne LiDAR returns | null | null | null | null | q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Light detection and ranging (LiDAR) provides information on the vertical
structure of forest stands enabling detailed and extensive ecosystem study. The
vertical structure is often summarized by scalar features and data-reduction
techniques that limit the interpretation of results. Instead, we quantified the
influence of three variables, species, crown cover, and age, on the vertical
distribution of airborne LiDAR returns from forest stands. We studied 5,428
regular, even-aged stands in Quebec (Canada) with five dominant species: balsam
fir (Abies balsamea (L.) Mill.), paper birch (Betula papyrifera Marsh), black
spruce (Picea mariana (Mill.) BSP), white spruce (Picea glauca Moench) and
aspen (Populus tremuloides Michx.). We modeled the vertical distribution
against the three variables using a functional general linear model and a novel
nonparametric graphical test of significance. Results indicate that LiDAR
returns from aspen stands had the most uniform vertical distribution. Balsam
fir and white birch distributions were similar and centered at around 50% of
the stand height, and black spruce and white spruce distributions were skewed
to below 30% of stand height (p<0.001). Increased crown cover concentrated the
distributions around 50% of stand height. Increasing age gradually shifted the
distributions higher in the stand for stands younger than 70-years, before
plateauing and slowly declining at 90-120 years. Results suggest that the
vertical distributions of LiDAR returns depend on the three variables studied.
| [
{
"created": "Sun, 11 Apr 2021 17:15:47 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Jun 2021 01:40:44 GMT",
"version": "v2"
}
] | 2021-06-03 | [
[
"Racine",
"Etienne",
""
],
[
"Coops",
"Nicholas C.",
""
],
[
"Bégin",
"Jean",
""
],
[
"Myllymäki",
"Mari",
""
]
] | Light detection and ranging (LiDAR) provides information on the vertical structure of forest stands enabling detailed and extensive ecosystem study. The vertical structure is often summarized by scalar features and data-reduction techniques that limit the interpretation of results. Instead, we quantified the influence of three variables, species, crown cover, and age, on the vertical distribution of airborne LiDAR returns from forest stands. We studied 5,428 regular, even-aged stands in Quebec (Canada) with five dominant species: balsam fir (Abies balsamea (L.) Mill.), paper birch (Betula papyrifera Marsh), black spruce (Picea mariana (Mill.) BSP), white spruce (Picea glauca Moench) and aspen (Populus tremuloides Michx.). We modeled the vertical distribution against the three variables using a functional general linear model and a novel nonparametric graphical test of significance. Results indicate that LiDAR returns from aspen stands had the most uniform vertical distribution. Balsam fir and white birch distributions were similar and centered at around 50% of the stand height, and black spruce and white spruce distributions were skewed to below 30% of stand height (p<0.001). Increased crown cover concentrated the distributions around 50% of stand height. Increasing age gradually shifted the distributions higher in the stand for stands younger than 70-years, before plateauing and slowly declining at 90-120 years. Results suggest that the vertical distributions of LiDAR returns depend on the three variables studied. |
1511.09062 | Viet Chi Tran | Sylvain Billiard, Pierre Collet, R\'egis Ferri\`ere, Sylvie
M\'el\'eard, Viet Chi Tran | The effect of competition and horizontal trait inheritance on invasion,
fixation and polymorphism | 1 Electronic Supplementary Material | null | null | null | q-bio.PE math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Horizontal transfer (HT) of heritable information or `traits' (carried by
genetic elements, endosymbionts, or culture) is widespread among living
organisms. Yet current ecological and evolutionary theory addressing HT is
limited. We present a modeling framework for the dynamics of two populations
that compete for resources and exchange horizontally (transfer) an otherwise
vertically inherited trait. Competition influences individual demographics,
affecting population size, which feeds back on the dynamics of transfer. We
capture this feedback with a stochastic individual-based model, from which we
derive a deterministic approximation for large populations. The interaction
between horizontal transfer and competition makes possible the stable (or
bi-stable) polymorphic maintenance of deleterious traits (including costly
plasmids). When transfer rates are of a general density-dependent form,
transfer stochasticity contributes strongly to population fluctuations. For an
initially rare trait, we describe the probabilistic dynamics of invasion and
fixation. Acceleration of fixation by HT is faster when competition is weak in
the resident population. Thus, HT can have a major impact on the distribution
of mutational effects that are fixed, and our model provides a basis for a
general theory of the influence of HT on eco-evolutionary dynamics and
adaptation.
| [
{
"created": "Sun, 29 Nov 2015 18:55:30 GMT",
"version": "v1"
}
] | 2015-12-01 | [
[
"Billiard",
"Sylvain",
""
],
[
"Collet",
"Pierre",
""
],
[
"Ferrière",
"Régis",
""
],
[
"Méléard",
"Sylvie",
""
],
[
"Tran",
"Viet Chi",
""
]
] | Horizontal transfer (HT) of heritable information or `traits' (carried by genetic elements, endosymbionts, or culture) is widespread among living organisms. Yet current ecological and evolutionary theory addressing HT is limited. We present a modeling framework for the dynamics of two populations that compete for resources and exchange horizontally (transfer) an otherwise vertically inherited trait. Competition influences individual demographics, affecting population size, which feeds back on the dynamics of transfer. We capture this feedback with a stochastic individual-based model, from which we derive a deterministic approximation for large populations. The interaction between horizontal transfer and competition makes possible the stable (or bi-stable) polymorphic maintenance of deleterious traits (including costly plasmids). When transfer rates are of a general density-dependent form, transfer stochasticity contributes strongly to population fluctuations. For an initially rare trait, we describe the probabilistic dynamics of invasion and fixation. Acceleration of fixation by HT is faster when competition is weak in the resident population. Thus, HT can have a major impact on the distribution of mutational effects that are fixed, and our model provides a basis for a general theory of the influence of HT on eco-evolutionary dynamics and adaptation. |
1211.4911 | Justin Yeakel | Justin D. Yeakel, Paulo R. Guimaraes Jr, Herve Bocherens, Paul L. Koch | The impact of climate change on the structure of Pleistocene mammoth
steppe food webs | null | null | 10.1098/rspb.2013.0239 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Species interactions shape predator-prey networks, impacting community
structure and, potentially, ecological dynamics. It is likely that global
climatic perturbations that occur over long periods of time have a significant
impact on species interactions patterns. However, observations of how these
patterns change over time are typically limited to extant communities, which is
particularly problematic for communities with long-lived species. Here we
integrate stable isotope analysis and network theory to reconstruct patterns of
trophic interactions for six independent mammalian communities that inhabited
mammoth steppe environments spanning western Europe to eastern Alaska during
the Pleistocene. We use a Bayesian mixing model to quantify the proportional
contribution of prey to the diets of local predators, and assess how the
structure of trophic interactions changed across space and the Last Glacial
Maximum (LGM), a global climatic event that severely impacted mammoth steppe
communities. We find that large felids had diets that were more constrained
than other co-occurring predators, and largely influenced by an increase in
{\it Rangifer} abundance after the LGM. Moreover, the structural organization
of Beringian and European communities strongly differed: compared to Europe,
species interactions in Beringian communities before the LGM were highly
compartmentalized, or modular. This modularity was lost during the LGM, and
partially recovered after the glacial retreat, and we suggest that changes in
modularity among predators and prey may have been driven by geographic
insularity.
| [
{
"created": "Wed, 21 Nov 2012 01:43:00 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Jan 2013 01:25:36 GMT",
"version": "v2"
}
] | 2015-03-13 | [
[
"Yeakel",
"Justin D.",
""
],
[
"Guimaraes",
"Paulo R.",
"Jr"
],
[
"Bocherens",
"Herve",
""
],
[
"Koch",
"Paul L.",
""
]
] | Species interactions shape predator-prey networks, impacting community structure and, potentially, ecological dynamics. It is likely that global climatic perturbations that occur over long periods of time have a significant impact on species interactions patterns. However, observations of how these patterns change over time are typically limited to extant communities, which is particularly problematic for communities with long-lived species. Here we integrate stable isotope analysis and network theory to reconstruct patterns of trophic interactions for six independent mammalian communities that inhabited mammoth steppe environments spanning western Europe to eastern Alaska during the Pleistocene. We use a Bayesian mixing model to quantify the proportional contribution of prey to the diets of local predators, and assess how the structure of trophic interactions changed across space and the Last Glacial Maximum (LGM), a global climatic event that severely impacted mammoth steppe communities. We find that large felids had diets that were more constrained than other co-occurring predators, and largely influenced by an increase in {\it Rangifer} abundance after the LGM. Moreover, the structural organization of Beringian and European communities strongly differed: compared to Europe, species interactions in Beringian communities before the LGM were highly compartmentalized, or modular. This modularity was lost during the LGM, and partially recovered after the glacial retreat, and we suggest that changes in modularity among predators and prey may have been driven by geographic insularity. |
1712.03377 | Roland Kr\"amer | Ulrich Warttinger, Christina Giese, Roland Kr\"amer | Comparison of Heparin Red, Azure A and Toluidine Blue assays for direct
quantification of heparins in human plasma | 15 pages, 4 figure, 3 schemes, 1 table | null | null | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heparins are are sulfated polysaccharides that have tremendous clinical
importance as anticoagulant drugs. Monitoring of heparin blood levels can
improve patient safety. In clinical practice, heparins are monitored indirectly
by their inhibtory effect on coagulation proteases. Drawbacks of these
established methods have stimulated the development of simple direct detection
methods with cationic dyes that change absorbance or fluorescence upon binding
of polyanionic heparin. Very few such dyes or assay kits, however, are
commercially and widely available to a broad community of researchers and
clinicians. This study compares the performance of three commercial dyes for
the direct quantification of unfractionated heparin and the widely used
low-molecular-weight heparin enoxaparin. Two traditional metachromatic dyes,
Azure A and Toluidine Blue, and the more recently developed fluorescent dye
Heparin Red were applied in a mix-and-read microplate assay to the same
heparin-spiked human plasma samples. In the clinically most relevant
concentration range below 1 IU (international units) per mL, only Heparin Red
is a useful tool for the determination of both heparins. Heparin Red is at
least 9 times more sensitive than the metachromatic dyes which can not reliably
quantify the heparins in this concentration range. Unfractionated heparin
levels between 2 and 10 IU per mL can be determined by all dyes, Heparin Red
being the most sensitive.
| [
{
"created": "Sat, 9 Dec 2017 11:41:12 GMT",
"version": "v1"
}
] | 2017-12-12 | [
[
"Warttinger",
"Ulrich",
""
],
[
"Giese",
"Christina",
""
],
[
"Krämer",
"Roland",
""
]
] | Heparins are are sulfated polysaccharides that have tremendous clinical importance as anticoagulant drugs. Monitoring of heparin blood levels can improve patient safety. In clinical practice, heparins are monitored indirectly by their inhibtory effect on coagulation proteases. Drawbacks of these established methods have stimulated the development of simple direct detection methods with cationic dyes that change absorbance or fluorescence upon binding of polyanionic heparin. Very few such dyes or assay kits, however, are commercially and widely available to a broad community of researchers and clinicians. This study compares the performance of three commercial dyes for the direct quantification of unfractionated heparin and the widely used low-molecular-weight heparin enoxaparin. Two traditional metachromatic dyes, Azure A and Toluidine Blue, and the more recently developed fluorescent dye Heparin Red were applied in a mix-and-read microplate assay to the same heparin-spiked human plasma samples. In the clinically most relevant concentration range below 1 IU (international units) per mL, only Heparin Red is a useful tool for the determination of both heparins. Heparin Red is at least 9 times more sensitive than the metachromatic dyes which can not reliably quantify the heparins in this concentration range. Unfractionated heparin levels between 2 and 10 IU per mL can be determined by all dyes, Heparin Red being the most sensitive. |
1202.0428 | Philipp Germann | Philipp Germann, Dzianis Menshykau, Simon Tanaka and Dagmar Iber | Simulating Organogenesis in COMSOL | Proceedings of COMSOL Conference, Stuttgart 2011 | null | null | null | q-bio.TO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Organogenesis is a tightly regulated process that has been studied
experimentally for decades. Computational models can help to integrate
available knowledge and to better understand the underlying regulatory logic.
We are currently studying mechanistic models for the development of limbs,
lungs, kidneys, and bone. We have tested a number of alternative methods to
solve our spatio- temporal differential equation models of reaction-diffusion
type on growing domains of realistic shape, among them finite elements in
COMSOL Multiphysics. Given the large number of variables (up to fifteen), the
sharp domain boundaries, the travelling wave character of some solutions, and
the stiffness of the reactions we are facing numerous numerical challenges. To
test new ideas efficiently we have developed a strategy to optimize simulation
times in COMSOL.
| [
{
"created": "Thu, 2 Feb 2012 13:34:05 GMT",
"version": "v1"
}
] | 2012-02-03 | [
[
"Germann",
"Philipp",
""
],
[
"Menshykau",
"Dzianis",
""
],
[
"Tanaka",
"Simon",
""
],
[
"Iber",
"Dagmar",
""
]
] | Organogenesis is a tightly regulated process that has been studied experimentally for decades. Computational models can help to integrate available knowledge and to better understand the underlying regulatory logic. We are currently studying mechanistic models for the development of limbs, lungs, kidneys, and bone. We have tested a number of alternative methods to solve our spatio- temporal differential equation models of reaction-diffusion type on growing domains of realistic shape, among them finite elements in COMSOL Multiphysics. Given the large number of variables (up to fifteen), the sharp domain boundaries, the travelling wave character of some solutions, and the stiffness of the reactions we are facing numerous numerical challenges. To test new ideas efficiently we have developed a strategy to optimize simulation times in COMSOL. |
2211.11808 | Lana Garmire | Lana X. Garmire, Yijun Li, Qianhui Huang, Chuan Xu, Sarah Teichmann,
Naftali Kaminski, Matteo Pellegrini, Quan Nguyen, Andrew E. Teschendorff | Challenges and perspectives in computational deconvolution of genomics
data | null | null | null | null | q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | Deciphering cell type heterogeneity is crucial for systematically
understanding tissue homeostasis and its dysregulation in diseases.
Computational deconvolution is an efficient approach estimating cell type
abundances from a variety of omics data. Despite significant methodological
progress in computational deconvolution in recent years, challenges are still
outstanding. Here we enlist four significant challenges related to
computational deconvolution, from the quality of the reference data, generation
of ground truth data, limitations of computational methodologies, and
benchmarking design and implementation. Finally, we make recommendations on
reference data generation, new directions of computational methodologies and
strategies to promote rigorous benchmarking.
| [
{
"created": "Mon, 21 Nov 2022 19:18:06 GMT",
"version": "v1"
},
{
"created": "Sat, 2 Sep 2023 16:51:48 GMT",
"version": "v2"
}
] | 2023-09-06 | [
[
"Garmire",
"Lana X.",
""
],
[
"Li",
"Yijun",
""
],
[
"Huang",
"Qianhui",
""
],
[
"Xu",
"Chuan",
""
],
[
"Teichmann",
"Sarah",
""
],
[
"Kaminski",
"Naftali",
""
],
[
"Pellegrini",
"Matteo",
""
],
[
"... | Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach estimating cell type abundances from a variety of omics data. Despite significant methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four significant challenges related to computational deconvolution, from the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies and strategies to promote rigorous benchmarking. |
2210.16577 | Jean-Baptiste Camps | Jean-Baptiste Camps and Julien Randon-Furling | Lost Manuscripts and Extinct Texts: A Dynamic Model of Cultural
Transmission | null | null | null | null | q-bio.PE stat.AP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How did written works evolve, disappear or survive down through the ages? In
this paper, we propose a unified, formal framework for two fundamental
questions in the study of the transmission of texts: how much was lost or
preserved from all works of the past, and why do their genealogies (their
``phylogenetic trees'') present the very peculiar shapes that we observe or,
more precisely, reconstruct? We argue here that these questions share
similarities to those encountered in evolutionary biology, and can be described
in terms of ``genetic'' drift and ``natural'' selection. Through agent-based
models, we show that such properties as have been observed by philologists
since the 1800s can be simulated, and confronted to data gathered for ancient
and medieval texts across Europe, in order to obtain plausible estimations of
the number of works and manuscripts that existed and were lost.
| [
{
"created": "Sat, 29 Oct 2022 11:47:07 GMT",
"version": "v1"
}
] | 2022-11-01 | [
[
"Camps",
"Jean-Baptiste",
""
],
[
"Randon-Furling",
"Julien",
""
]
] | How did written works evolve, disappear or survive down through the ages? In this paper, we propose a unified, formal framework for two fundamental questions in the study of the transmission of texts: how much was lost or preserved from all works of the past, and why do their genealogies (their ``phylogenetic trees'') present the very peculiar shapes that we observe or, more precisely, reconstruct? We argue here that these questions share similarities to those encountered in evolutionary biology, and can be described in terms of ``genetic'' drift and ``natural'' selection. Through agent-based models, we show that such properties as have been observed by philologists since the 1800s can be simulated, and confronted to data gathered for ancient and medieval texts across Europe, in order to obtain plausible estimations of the number of works and manuscripts that existed and were lost. |
1811.12245 | Greg Murray | Greg Murray, Catherine Orr, Jamie E. M. Byrne, Matthew E. Hughes,
Susan L. Rossell, Sheri L. Johnson | Effect of time of day on reward circuitry: Further thoughts on methods,
prompted by Steel et al 2018 | 9 pages, 1 figure | null | null | null | q-bio.NC | http://creativecommons.org/publicdomain/zero/1.0/ | The interplay between circadian and reward function is well understood in
animal models, and is of growing interest as an aetiological explanation in
psychopathologies. Circadian modulation of reward function has been
demonstrated in human behavioural data, but understanding at the neural level
is limited. In 2017, our group published results of a first step in addressing
this deficit, demonstrating a diurnal rhythm in fMRI-measured reward
activation. In 2018, Steel et al wrote a constructive critique of our findings,
and the aim of this paper is to outline how future research could improve on
our first proof-of-concept study. Key challenges include addressing divergent
and convergent validity (by addressing non-reward neural variation, and testing
for absence of variation in threat-related pathways), preregistration and power
analysis to protect against false positives, wider range of fMRI methods (to
directly test our post-hoc hypothesis of some form of reward prediction error,
and multiple phases of reward), the parallel collection of behavioural data
(particularly self-reported positive affect, and actigraphically measured
activity) to illuminate the nature of the reward activation across the day, and
some attempt to parse out circadian versus homeostatic/masking influences on
any observed diurnal rhythm in neural reward activation.
| [
{
"created": "Wed, 14 Nov 2018 01:55:57 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Dec 2018 05:05:49 GMT",
"version": "v2"
}
] | 2018-12-12 | [
[
"Murray",
"Greg",
""
],
[
"Orr",
"Catherine",
""
],
[
"Byrne",
"Jamie E. M.",
""
],
[
"Hughes",
"Matthew E.",
""
],
[
"Rossell",
"Susan L.",
""
],
[
"Johnson",
"Sheri L.",
""
]
] | The interplay between circadian and reward function is well understood in animal models, and is of growing interest as an aetiological explanation in psychopathologies. Circadian modulation of reward function has been demonstrated in human behavioural data, but understanding at the neural level is limited. In 2017, our group published results of a first step in addressing this deficit, demonstrating a diurnal rhythm in fMRI-measured reward activation. In 2018, Steel et al wrote a constructive critique of our findings, and the aim of this paper is to outline how future research could improve on our first proof-of-concept study. Key challenges include addressing divergent and convergent validity (by addressing non-reward neural variation, and testing for absence of variation in threat-related pathways), preregistration and power analysis to protect against false positives, wider range of fMRI methods (to directly test our post-hoc hypothesis of some form of reward prediction error, and multiple phases of reward), the parallel collection of behavioural data (particularly self-reported positive affect, and actigraphically measured activity) to illuminate the nature of the reward activation across the day, and some attempt to parse out circadian versus homeostatic/masking influences on any observed diurnal rhythm in neural reward activation. |
2206.07966 | Yu Qin | Yu Qin and Alex Sheremet | Mesoscopic Collective Activity in Excitatory Neural Fields: Governing
Equations | 27 pages, 7 figures | null | null | null | q-bio.QM q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | In this study we derive the governing equations for mesoscopic collective
activity in the cortex, starting from the generic Hodgkin-Huxley equations for
microscopic cell dynamics. For simplicity, and to maintain focus on the
essential elements of the derivation, the discussion is confined to excitatory
neural fields. The fundamental assumption of the procedure is that mesoscale
processes are macroscopic with respect to cell-scale activity, and emerge as
the average behavior of a large population of cells. Because of their duration,
action-potential details are assumed not observable at mesoscale; the essential
mesoscopic function of action potentials is to redistribute energy in the
neural field. The Hodgkin-Huxley dynamical model is first reduced to a set of
equations that describe subthreshold dynamics. An ensemble average over a cell
population then produces a closed system of equations involving two mesoscopic
state variables: the density of kinetic energy J, carried by sodium ionic
currents, and the excitability H of the neural field, which could be described
as the average state of gating variable h. The resulting model is represented
as essentially a subthreshold process; and the dynamical role of the firing
rate is naturally reassessed as describing energy transfers. The linear
properties of the equations are consistent with expectations for the dynamics
of excitatory neural fields: the system supports oscillations of progressive
waves, with shorter waves typically having higher frequencies, propagating
slower, and decaying faster. Extending the derivation to include more complex
cell dynamics (e.g., including other ionic channels, e.g., calcium channels)
and multiple-type, excitatory-inhibitory, neural fields is straightforward, and
will be presented elsewhere.
| [
{
"created": "Thu, 16 Jun 2022 07:13:33 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Jul 2022 17:50:09 GMT",
"version": "v2"
}
] | 2022-07-07 | [
[
"Qin",
"Yu",
""
],
[
"Sheremet",
"Alex",
""
]
] | In this study we derive the governing equations for mesoscopic collective activity in the cortex, starting from the generic Hodgkin-Huxley equations for microscopic cell dynamics. For simplicity, and to maintain focus on the essential elements of the derivation, the discussion is confined to excitatory neural fields. The fundamental assumption of the procedure is that mesoscale processes are macroscopic with respect to cell-scale activity, and emerge as the average behavior of a large population of cells. Because of their duration, action-potential details are assumed not observable at mesoscale; the essential mesoscopic function of action potentials is to redistribute energy in the neural field. The Hodgkin-Huxley dynamical model is first reduced to a set of equations that describe subthreshold dynamics. An ensemble average over a cell population then produces a closed system of equations involving two mesoscopic state variables: the density of kinetic energy J, carried by sodium ionic currents, and the excitability H of the neural field, which could be described as the average state of gating variable h. The resulting model is represented as essentially a subthreshold process; and the dynamical role of the firing rate is naturally reassessed as describing energy transfers. The linear properties of the equations are consistent with expectations for the dynamics of excitatory neural fields: the system supports oscillations of progressive waves, with shorter waves typically having higher frequencies, propagating slower, and decaying faster. Extending the derivation to include more complex cell dynamics (e.g., including other ionic channels, e.g., calcium channels) and multiple-type, excitatory-inhibitory, neural fields is straightforward, and will be presented elsewhere. |
2405.07837 | Troy Shinbrot | Troy Shinbrot and Wise Young | Why Decussate? Topological Constraints on 3D Wiring | 15 pages, 8 figures | The Anatomical Record 291.10 (2008) 1278-1292 | 10.1002/ar.20731 | null | q-bio.NC cond-mat.dis-nn math.GT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Many vertebrate motor and sensory systems decussate, or cross the midline to
the opposite side of the body. The successful crossing of millions of axons
during development requires a complex of tightly controlled regulatory
processes. Because these processes have evolved in many distinct systems and
organisms, it seems reasonable to presume that decussation confers a
significant functional advantage. Yet if this is so, the nature of this
advantage is not understood. In this article, we examine constraints imposed by
topology on the ways that a three-dimensional processor and environment can be
wired together in a continuous, somatotopic, way. We show that as the number of
wiring connections grows, decussated arrangements become overwhelmingly more
robust against wiring errors than seemingly simpler same-sided wiring schemes.
These results provide a predictive approach for understanding how 3D networks
must be wired if they are to be robust, and therefore have implications both
for future large-scale computational networks and for complex bio-medical
devices
| [
{
"created": "Mon, 13 May 2024 15:24:11 GMT",
"version": "v1"
}
] | 2024-05-14 | [
[
"Shinbrot",
"Troy",
""
],
[
"Young",
"Wise",
""
]
] | Many vertebrate motor and sensory systems decussate, or cross the midline to the opposite side of the body. The successful crossing of millions of axons during development requires a complex of tightly controlled regulatory processes. Because these processes have evolved in many distinct systems and organisms, it seems reasonable to presume that decussation confers a significant functional advantage. Yet if this is so, the nature of this advantage is not understood. In this article, we examine constraints imposed by topology on the ways that a three-dimensional processor and environment can be wired together in a continuous, somatotopic, way. We show that as the number of wiring connections grows, decussated arrangements become overwhelmingly more robust against wiring errors than seemingly simpler same-sided wiring schemes. These results provide a predictive approach for understanding how 3D networks must be wired if they are to be robust, and therefore have implications both for future large-scale computational networks and for complex bio-medical devices |
2004.00553 | Antonio Della Cioppa | I. De Falco, A. Della Cioppa, U. Scafuri, and E. Tarantino | Coronavirus Covid-19 spreading in Italy: optimizing an epidemiological
model with dynamic social distancing through Differential Evolution | null | null | null | null | q-bio.PE cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The aim of this paper consists in the application of a recent epidemiological
model, namely SEIR with Social Distancing (SEIR--SD), extended here through the
definition of a social distancing function varying over time, to assess the
situation related to the spreading of the coronavirus Covid--19 in Italy and in
two of its most important regions, i.e., Lombardy and Campania. To profitably
use this model, the most suitable values of its parameters must be found. The
estimation of the SEIR--SD model parameters takes place here through the use of
Differential Evolution, a heuristic optimization technique. In this way, we are
able to evaluate for each of the three above-mentioned scenarios the daily
number of infectious cases from today until the end of virus spreading, the
day(s) in which this number will be at its highest peak, and the day in which
the infected cases will become very close to zero.
| [
{
"created": "Wed, 1 Apr 2020 16:32:58 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Apr 2020 17:15:00 GMT",
"version": "v2"
},
{
"created": "Sat, 4 Apr 2020 17:50:17 GMT",
"version": "v3"
}
] | 2020-04-07 | [
[
"De Falco",
"I.",
""
],
[
"Della Cioppa",
"A.",
""
],
[
"Scafuri",
"U.",
""
],
[
"Tarantino",
"E.",
""
]
] | The aim of this paper consists in the application of a recent epidemiological model, namely SEIR with Social Distancing (SEIR--SD), extended here through the definition of a social distancing function varying over time, to assess the situation related to the spreading of the coronavirus Covid--19 in Italy and in two of its most important regions, i.e., Lombardy and Campania. To profitably use this model, the most suitable values of its parameters must be found. The estimation of the SEIR--SD model parameters takes place here through the use of Differential Evolution, a heuristic optimization technique. In this way, we are able to evaluate for each of the three above-mentioned scenarios the daily number of infectious cases from today until the end of virus spreading, the day(s) in which this number will be at its highest peak, and the day in which the infected cases will become very close to zero. |
1711.11314 | Vince Grolmusz | Mate Fellner and Balint Varga and Vince Grolmusz | The Frequent Subgraphs of the Connectome of the Human Brain | null | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In mapping the human structural connectome, we are in a very fortunate
situation: one can compute and compare graphs, describing the cerebral
connections between the very same, anatomically identified small regions of the
gray matter among hundreds of human subjects. The comparison of these graphs
has led to numerous recent results, as the (i) discovery that women's
connectomes have deeper and richer connectivity-related graph parameters like
those of men, or (ii) the description of more and less conservatively connected
lobes and cerebral regions, and (iii) the discovery of the phenomenon of the
Consensus Connectome Dynamics.
Today one of the greatest challenges of brain science is the description and
modeling of the circuitry of the human brain. For this goal, we need to
identify sub-circuits that are present in almost all human subjects and those,
which are much less frequent: the former sub-circuits most probably have
functions with general importance, the latter sub-circuits are probably related
to the individual variability of the brain structure and functions. The present
contribution describes the frequent connected subgraphs (instead of
sub-circuits) of at most 6 edges in the human brain. We analyze these frequent
graphs and also examine sex differences in these graphs: we demonstrate
numerous connected sub-graphs that are more frequent in female or the male
connectome. While our results describe subgraphs, instead of sub-circuits, we
need to note that all macroscopic sub-circuits correspond to an underlying
connected subgraph.
Our data source is the public release of the Human Connectome Project, and we
are applying the data of 426 human subjects in this study.
| [
{
"created": "Thu, 30 Nov 2017 10:45:43 GMT",
"version": "v1"
}
] | 2017-12-01 | [
[
"Fellner",
"Mate",
""
],
[
"Varga",
"Balint",
""
],
[
"Grolmusz",
"Vince",
""
]
] | In mapping the human structural connectome, we are in a very fortunate situation: one can compute and compare graphs, describing the cerebral connections between the very same, anatomically identified small regions of the gray matter among hundreds of human subjects. The comparison of these graphs has led to numerous recent results, as the (i) discovery that women's connectomes have deeper and richer connectivity-related graph parameters like those of men, or (ii) the description of more and less conservatively connected lobes and cerebral regions, and (iii) the discovery of the phenomenon of the Consensus Connectome Dynamics. Today one of the greatest challenges of brain science is the description and modeling of the circuitry of the human brain. For this goal, we need to identify sub-circuits that are present in almost all human subjects and those, which are much less frequent: the former sub-circuits most probably have functions with general importance, the latter sub-circuits are probably related to the individual variability of the brain structure and functions. The present contribution describes the frequent connected subgraphs (instead of sub-circuits) of at most 6 edges in the human brain. We analyze these frequent graphs and also examine sex differences in these graphs: we demonstrate numerous connected sub-graphs that are more frequent in female or the male connectome. While our results describe subgraphs, instead of sub-circuits, we need to note that all macroscopic sub-circuits correspond to an underlying connected subgraph. Our data source is the public release of the Human Connectome Project, and we are applying the data of 426 human subjects in this study. |
0904.2500 | Edward O'Brien Jr. | Edward P. O'Brien, Bernard R. Brooks, and Dave Thirumalai | Molecular origin of constant m-values, denatured state collapse, and
residue-dependent transition midpoints in globular proteins | 41 pages, 10 figures | null | null | null | q-bio.BM cond-mat.soft | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experiments show that for many two state folders the free energy of the
native state DG_ND([C]) changes linearly as the denaturant concentration [C] is
varied. The slope, m = d DG_ND([C])/d[C], is nearly constant. The m-value is
associated with the difference in the surface area between the native (N) and
the denatured (D) state, which should be a function of DR_g^2, the difference
in the square of the radius of gyration between the D and N states. Single
molecule experiments show that the denatured state undergoes an equilibrium
collapse transition as [C] decreases, which implies m also should be
[C]-dependent. We resolve the conundrum between constant m-values and
[C]-dependent changes in Rg using molecular simulations of a coarse-grained
representation of protein L, and the Molecular Transfer Model, for which the
equilibrium folding can be accurately calculated as a function of denaturant
concentration. We find that over a large range of denaturant concentration (> 3
M) the m-value is a constant, whereas under strongly renaturing conditions (< 3
M) it depends on [C]. The m-value is a constant above [C]> 3 M because the
[C]-dependent changes in the surface area of the backbone groups, which make
the largest contribution to m, is relatively narrow in the denatured state. The
burial of the backbone gives rise to substantial surface area changes below
[C]< 3 M, leading to collapse in the denatured state. The midpoint of
transition of individual residues vary significantly even though global folding
can be described as an all-or-none transition. Collapse driven by the loss of
favorable residue-solvent interactions and a concomitant increase in the
strength of intrapeptide interactions with decreasing [C]. These interactions
are non-uniformly distributed throughout the native structure of protein L.
| [
{
"created": "Thu, 16 Apr 2009 14:44:44 GMT",
"version": "v1"
}
] | 2009-04-20 | [
[
"O'Brien",
"Edward P.",
""
],
[
"Brooks",
"Bernard R.",
""
],
[
"Thirumalai",
"Dave",
""
]
] | Experiments show that for many two state folders the free energy of the native state DG_ND([C]) changes linearly as the denaturant concentration [C] is varied. The slope, m = d DG_ND([C])/d[C], is nearly constant. The m-value is associated with the difference in the surface area between the native (N) and the denatured (D) state, which should be a function of DR_g^2, the difference in the square of the radius of gyration between the D and N states. Single molecule experiments show that the denatured state undergoes an equilibrium collapse transition as [C] decreases, which implies m also should be [C]-dependent. We resolve the conundrum between constant m-values and [C]-dependent changes in Rg using molecular simulations of a coarse-grained representation of protein L, and the Molecular Transfer Model, for which the equilibrium folding can be accurately calculated as a function of denaturant concentration. We find that over a large range of denaturant concentration (> 3 M) the m-value is a constant, whereas under strongly renaturing conditions (< 3 M) it depends on [C]. The m-value is a constant above [C]> 3 M because the [C]-dependent changes in the surface area of the backbone groups, which make the largest contribution to m, is relatively narrow in the denatured state. The burial of the backbone gives rise to substantial surface area changes below [C]< 3 M, leading to collapse in the denatured state. The midpoint of transition of individual residues vary significantly even though global folding can be described as an all-or-none transition. Collapse driven by the loss of favorable residue-solvent interactions and a concomitant increase in the strength of intrapeptide interactions with decreasing [C]. These interactions are non-uniformly distributed throughout the native structure of protein L. |
2012.02734 | Arthur Genthon | Arthur Genthon and David Lacoste | Universal constraints on selection strength in lineage trees | null | Phys. Rev. Research 3, 023187 (2021) | 10.1103/PhysRevResearch.3.023187 | null | q-bio.PE cond-mat.stat-mech physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We obtain general inequalities constraining the difference between the
average of an arbitrary function of a phenotypic trait, which includes the
fitness landscape of the trait itself, in the presence or in the absence of
natural selection. These inequalities imply bounds on the strength of
selection, which can be measured from the statistics of trait values and
divisions along lineages. The upper bound is related to recent generalizations
of linear response relations in Stochastic Thermodynamics, and shares common
features with Fisher's fundamental theorem of natural selection, and with its
generalization by Price, although they define different measures of selection.
The lower bound follows from recent improvements on Jensen's inequality, and
both bounds depend on the variability of the fitness landscape. We illustrate
our results using numerical simulations of growing cell colonies and with
experimental data of time-lapse microscopy experiments of bacteria cell
colonies.
| [
{
"created": "Fri, 4 Dec 2020 17:27:44 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Mar 2021 08:43:34 GMT",
"version": "v2"
}
] | 2021-06-16 | [
[
"Genthon",
"Arthur",
""
],
[
"Lacoste",
"David",
""
]
] | We obtain general inequalities constraining the difference between the average of an arbitrary function of a phenotypic trait, which includes the fitness landscape of the trait itself, in the presence or in the absence of natural selection. These inequalities imply bounds on the strength of selection, which can be measured from the statistics of trait values and divisions along lineages. The upper bound is related to recent generalizations of linear response relations in Stochastic Thermodynamics, and shares common features with Fisher's fundamental theorem of natural selection, and with its generalization by Price, although they define different measures of selection. The lower bound follows from recent improvements on Jensen's inequality, and both bounds depend on the variability of the fitness landscape. We illustrate our results using numerical simulations of growing cell colonies and with experimental data of time-lapse microscopy experiments of bacteria cell colonies. |
1609.07000 | Bartosz Rozycki | Bartosz Rozycki and Marek Cieplak | Stiffness of the C-terminal disordered linker affects the geometry of
the active site in endoglucanase Cel8A | accepted for publication in Molecular BioSystems (September 22, 2016) | null | 10.1039/C6MB00606J | null | q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cellulosomes are complex multi-enzyme machineries which efficiently degrade
plant cell-wall polysaccharides. The multiple domains of the cellulosome
proteins are often tethered together by intrinsically disordered regions. The
properties and functions of these disordered linkers are not well understood.
In this work, we study endoglucanase Cel8A, which is a relevant enzymatic
component of the cellulosomes of Clostridium thermocellum. We use both all-atom
and coarse-grained simulations to investigate how the equilibrium conformations
of the catalytic domain of Cel8A are affected by the disordered linker at its C
terminus. We find that when the endoglucanase is bound to its substrate, the
effective stiffness of the linker can influence the distances between groups of
amino-acid residues throughout the entire enzymatic domain. In particular,
variations in the linker stiffness can lead to small changes in the geometry of
the active-site cleft. We suggest that such geometrical changes may, in turn,
have an effect on the catalytic activity of the enzyme.
| [
{
"created": "Thu, 22 Sep 2016 14:40:33 GMT",
"version": "v1"
}
] | 2016-09-23 | [
[
"Rozycki",
"Bartosz",
""
],
[
"Cieplak",
"Marek",
""
]
] | Cellulosomes are complex multi-enzyme machineries which efficiently degrade plant cell-wall polysaccharides. The multiple domains of the cellulosome proteins are often tethered together by intrinsically disordered regions. The properties and functions of these disordered linkers are not well understood. In this work, we study endoglucanase Cel8A, which is a relevant enzymatic component of the cellulosomes of Clostridium thermocellum. We use both all-atom and coarse-grained simulations to investigate how the equilibrium conformations of the catalytic domain of Cel8A are affected by the disordered linker at its C terminus. We find that when the endoglucanase is bound to its substrate, the effective stiffness of the linker can influence the distances between groups of amino-acid residues throughout the entire enzymatic domain. In particular, variations in the linker stiffness can lead to small changes in the geometry of the active-site cleft. We suggest that such geometrical changes may, in turn, have an effect on the catalytic activity of the enzyme. |
1708.03666 | Sarah Sauv\'e | Sarah A. Sauv\'e and Marcus T. Pearce | Attention but not musical training affects auditory streaming | 36 pages, 6 figures | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While musicians generally perform better than non-musicians in various
auditory discrimination tasks, effects of specific instrumental training have
received little attention. The effects of instrument-specific musical training
on auditory grouping in the context of stream segregation are investigated here
in three experiments. In Experiment 1a, participants listened to sequences of
ABA tones and indicated when they heard a change in rhythm. This change is
caused by the manipulation of the B tones' timbre and indexes a change in
perception from integration to segregation, or vice versa. While it was
expected that musicians would detect a change in rhythm earlier when their own
instrument was involved, no such pattern was observed. In Experiment 1b,
designed to control for potential expectation effects in Experiment 1a,
participants heard sequences of static ABA tones and reported their initial
perceptions, whether the sequence was integrated or segregated. Results show
that participants tend to initially perceive these static sequences as
segregated, and that perception is influenced by similarity between the timbres
involved. Finally, in Experiment 2 violinists and flautists located mistuned
notes in an interleaved melody paradigm containing a violin and a flute melody.
Performance did not depend on the instrument the participant played but rather
which melody their attention was directed to. Taken together, results from the
three experiments suggest that the specific instrument one practices does not
have an influence on auditory grouping, but attentional mechanisms are
necessary for processing auditory scenes.
| [
{
"created": "Fri, 11 Aug 2017 19:12:46 GMT",
"version": "v1"
}
] | 2017-08-15 | [
[
"Sauvé",
"Sarah A.",
""
],
[
"Pearce",
"Marcus T.",
""
]
] | While musicians generally perform better than non-musicians in various auditory discrimination tasks, effects of specific instrumental training have received little attention. The effects of instrument-specific musical training on auditory grouping in the context of stream segregation are investigated here in three experiments. In Experiment 1a, participants listened to sequences of ABA tones and indicated when they heard a change in rhythm. This change is caused by the manipulation of the B tones' timbre and indexes a change in perception from integration to segregation, or vice versa. While it was expected that musicians would detect a change in rhythm earlier when their own instrument was involved, no such pattern was observed. In Experiment 1b, designed to control for potential expectation effects in Experiment 1a, participants heard sequences of static ABA tones and reported their initial perceptions, whether the sequence was integrated or segregated. Results show that participants tend to initially perceive these static sequences as segregated, and that perception is influenced by similarity between the timbres involved. Finally, in Experiment 2 violinists and flautists located mistuned notes in an interleaved melody paradigm containing a violin and a flute melody. Performance did not depend on the instrument the participant played but rather which melody their attention was directed to. Taken together, results from the three experiments suggest that the specific instrument one practices does not have an influence on auditory grouping, but attentional mechanisms are necessary for processing auditory scenes. |
2004.04474 | Lorenzo Vannucci | Lorenzo Vannucci, Maria Pasquini, Cristina Spalletti, Matteo Caleo,
Silvestro Micera, Cecilia Laschi, Egidio Falotico | Towards in-silico robotic post-stroke rehabilitation for mice | 7 pages, 9 figures. To be published in the 2019 IEEE International
Conference on Cyborg and Bionic Systems | null | null | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The possibility of simulating in detail in-vivo experiments could be highly
beneficial to the neuroscientific community. It could easily allow for
preliminary testing of different experimental conditions without having to be
constrained by factors such as training of the subjects or resting times
between experimental trials. In order to achieve this, the simulation of the
environment, of the subject and of the neural system, should be as accurate as
possible. Unfortunately, it is not possible to completely simulate physical
systems, alongside their neural counterparts, without greatly increasing the
computational cost of the simulation. For this reason, it is crucial to limit
the simulation to all physical and neural areas that are involved in the
experiment. We propose that using a combination of data analysis and simulated
models is beneficial in determining the minimal subset of entities that have to
be included in the simulation to replicate the in-vivo experiment. In
particular, we focused on a pulling task performed by mice on a robotic
platform before and after lesion of the central nervous system. Here, we show
that, while it is possible to replicate the behaviour of the healthy mouse just
by including models of the mouse forelimb, spinal cord, and recording of the
rostral forelimb area (RFA), it is not possible to reproduce the behaviour of
the post-stroke mouse. This can give us insights on what other elements would
be needed to replicate the complete experiment.
| [
{
"created": "Thu, 9 Apr 2020 10:47:04 GMT",
"version": "v1"
}
] | 2020-04-10 | [
[
"Vannucci",
"Lorenzo",
""
],
[
"Pasquini",
"Maria",
""
],
[
"Spalletti",
"Cristina",
""
],
[
"Caleo",
"Matteo",
""
],
[
"Micera",
"Silvestro",
""
],
[
"Laschi",
"Cecilia",
""
],
[
"Falotico",
"Egidio",
""
... | The possibility of simulating in detail in-vivo experiments could be highly beneficial to the neuroscientific community. It could easily allow for preliminary testing of different experimental conditions without having to be constrained by factors such as training of the subjects or resting times between experimental trials. In order to achieve this, the simulation of the environment, of the subject and of the neural system, should be as accurate as possible. Unfortunately, it is not possible to completely simulate physical systems, alongside their neural counterparts, without greatly increasing the computational cost of the simulation. For this reason, it is crucial to limit the simulation to all physical and neural areas that are involved in the experiment. We propose that using a combination of data analysis and simulated models is beneficial in determining the minimal subset of entities that have to be included in the simulation to replicate the in-vivo experiment. In particular, we focused on a pulling task performed by mice on a robotic platform before and after lesion of the central nervous system. Here, we show that, while it is possible to replicate the behaviour of the healthy mouse just by including models of the mouse forelimb, spinal cord, and recording of the rostral forelimb area (RFA), it is not possible to reproduce the behaviour of the post-stroke mouse. This can give us insights on what other elements would be needed to replicate the complete experiment. |
2309.07766 | Benjamin Hayden | W. Jeffrey Johnston, Justin M. Fine, Seng Bum Michael Yoo, R. Becket
Ebitz, and Benjamin Y. Hayden | Semi-orthogonal subspaces for value mediate a tradeoff between binding
and generalization | arXiv admin note: substantial text overlap with arXiv:2205.06769 | null | null | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | When choosing between options, we must associate their values with the action
needed to select them. We hypothesize that the brain solves this binding
problem through neural population subspaces. To test this hypothesis, we
examined neuronal responses in five reward-sensitive regions in macaques
performing a risky choice task with sequential offers. Surprisingly, in all
areas, the neural population encoded the values of offers presented on the left
and right in distinct subspaces. We show that the encoding we observe is
sufficient to bind the values of the offers to their respective positions in
space while preserving abstract value information, which may be important for
rapid learning and generalization to novel contexts. Moreover, after both
offers have been presented, all areas encode the value of the first and second
offers in orthogonal subspaces. In this case as well, the orthogonalization
provides binding. Our binding-by-subspace hypothesis makes two novel
predictions borne out by the data. First, behavioral errors should correlate
with putative spatial (but not temporal) misbinding in the neural
representation. Second, the specific representational geometry that we observe
across animals also indicates that behavioral errors should increase when
offers have low or high values, compared to when they have medium values, even
when controlling for value difference. Together, these results support the idea
that the brain makes use of semi-orthogonal subspaces to bind features
together.
| [
{
"created": "Thu, 14 Sep 2023 14:54:25 GMT",
"version": "v1"
}
] | 2023-09-15 | [
[
"Johnston",
"W. Jeffrey",
""
],
[
"Fine",
"Justin M.",
""
],
[
"Yoo",
"Seng Bum Michael",
""
],
[
"Ebitz",
"R. Becket",
""
],
[
"Hayden",
"Benjamin Y.",
""
]
] | When choosing between options, we must associate their values with the action needed to select them. We hypothesize that the brain solves this binding problem through neural population subspaces. To test this hypothesis, we examined neuronal responses in five reward-sensitive regions in macaques performing a risky choice task with sequential offers. Surprisingly, in all areas, the neural population encoded the values of offers presented on the left and right in distinct subspaces. We show that the encoding we observe is sufficient to bind the values of the offers to their respective positions in space while preserving abstract value information, which may be important for rapid learning and generalization to novel contexts. Moreover, after both offers have been presented, all areas encode the value of the first and second offers in orthogonal subspaces. In this case as well, the orthogonalization provides binding. Our binding-by-subspace hypothesis makes two novel predictions borne out by the data. First, behavioral errors should correlate with putative spatial (but not temporal) misbinding in the neural representation. Second, the specific representational geometry that we observe across animals also indicates that behavioral errors should increase when offers have low or high values, compared to when they have medium values, even when controlling for value difference. Together, these results support the idea that the brain makes use of semi-orthogonal subspaces to bind features together. |
2004.00218 | Sukrit Gupta | Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman
Padmanabhan and Bal\'azs Guly\'as | 3D Deep Learning on Medical Images: A Review | Published in Sensors Journal
(https://www.mdpi.com/1424-8220/20/18/5097) | Sensors 2020, 20, 5097 | null | null | q-bio.QM cs.CV cs.LG eess.IV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The rapid advancements in machine learning, graphics processing technologies
and the availability of medical imaging data have led to a rapid increase in
the use of deep learning models in the medical domain. This was exacerbated by
the rapid advancements in convolutional neural network (CNN) based
architectures, which were adopted by the medical imaging community to assist
clinicians in disease diagnosis. Since the grand success of AlexNet in 2012,
CNNs have been increasingly used in medical image analysis to improve the
efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs
have been employed for the analysis of medical images. In this paper, we trace
the history of how the 3D CNN was developed from its machine learning roots, we
provide a brief mathematical description of 3D CNN and provide the
preprocessing steps required for medical images before feeding them to 3D CNNs.
We review the significant research in the field of 3D medical imaging analysis
using 3D CNNs (and its variants) in different medical areas such as
classification, segmentation, detection and localization. We conclude by
discussing the challenges associated with the use of 3D CNNs in the medical
imaging domain (and the use of deep learning models in general) and possible
future trends in the field.
| [
{
"created": "Wed, 1 Apr 2020 03:56:48 GMT",
"version": "v1"
},
{
"created": "Sat, 9 May 2020 05:26:19 GMT",
"version": "v2"
},
{
"created": "Sat, 11 Jul 2020 04:28:29 GMT",
"version": "v3"
},
{
"created": "Tue, 13 Oct 2020 08:38:19 GMT",
"version": "v4"
}
] | 2020-10-14 | [
[
"Singh",
"Satya P.",
""
],
[
"Wang",
"Lipo",
""
],
[
"Gupta",
"Sukrit",
""
],
[
"Goli",
"Haveesh",
""
],
[
"Padmanabhan",
"Parasuraman",
""
],
[
"Gulyás",
"Balázs",
""
]
] | The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field. |
1511.09426 | Cengiz Pehlevan | Cengiz Pehlevan, Dmitri B. Chklovskii | A Normative Theory of Adaptive Dimensionality Reduction in Neural
Networks | Advances in Neural Information Processing Systems (NIPS), 2015 | null | null | null | q-bio.NC cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To make sense of the world our brains must analyze high-dimensional datasets
streamed by our sensory organs. Because such analysis begins with
dimensionality reduction, modelling early sensory processing requires
biologically plausible online dimensionality reduction algorithms. Recently, we
derived such an algorithm, termed similarity matching, from a Multidimensional
Scaling (MDS) objective function. However, in the existing algorithm, the
number of output dimensions is set a priori by the number of output neurons and
cannot be changed. Because the number of informative dimensions in sensory
inputs is variable there is a need for adaptive dimensionality reduction. Here,
we derive biologically plausible dimensionality reduction algorithms which
adapt the number of output dimensions to the eigenspectrum of the input
covariance matrix. We formulate three objective functions which, in the offline
setting, are optimized by the projections of the input dataset onto its
principal subspace scaled by the eigenvalues of the output covariance matrix.
In turn, the output eigenvalues are computed as i) soft-thresholded, ii)
hard-thresholded, iii) equalized thresholded eigenvalues of the input
covariance matrix. In the online setting, we derive the three corresponding
adaptive algorithms and map them onto the dynamics of neuronal activity in
networks with biologically plausible local learning rules. Remarkably, in the
last two networks, neurons are divided into two classes which we identify with
principal neurons and interneurons in biological circuits.
| [
{
"created": "Mon, 30 Nov 2015 18:45:30 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Jan 2016 18:44:23 GMT",
"version": "v2"
}
] | 2016-01-27 | [
[
"Pehlevan",
"Cengiz",
""
],
[
"Chklovskii",
"Dmitri B.",
""
]
] | To make sense of the world our brains must analyze high-dimensional datasets streamed by our sensory organs. Because such analysis begins with dimensionality reduction, modelling early sensory processing requires biologically plausible online dimensionality reduction algorithms. Recently, we derived such an algorithm, termed similarity matching, from a Multidimensional Scaling (MDS) objective function. However, in the existing algorithm, the number of output dimensions is set a priori by the number of output neurons and cannot be changed. Because the number of informative dimensions in sensory inputs is variable there is a need for adaptive dimensionality reduction. Here, we derive biologically plausible dimensionality reduction algorithms which adapt the number of output dimensions to the eigenspectrum of the input covariance matrix. We formulate three objective functions which, in the offline setting, are optimized by the projections of the input dataset onto its principal subspace scaled by the eigenvalues of the output covariance matrix. In turn, the output eigenvalues are computed as i) soft-thresholded, ii) hard-thresholded, iii) equalized thresholded eigenvalues of the input covariance matrix. In the online setting, we derive the three corresponding adaptive algorithms and map them onto the dynamics of neuronal activity in networks with biologically plausible local learning rules. Remarkably, in the last two networks, neurons are divided into two classes which we identify with principal neurons and interneurons in biological circuits. |
2204.02731 | Emily SC Ching Prof. | Chumin Sun, K.C. Lin, C.Y. Yeung, Emily S.C. Ching, Yu-Ting Huang,
Pik-Yin Lai, C.K. Chan | Revealing directed effective connectivity of cortical neuronal networks
from measurements | null | null | 10.1103/PhysRevE.105.044406 | null | q-bio.NC physics.bio-ph | http://creativecommons.org/licenses/by/4.0/ | In the study of biological networks, one of the major challenges is to
understand the relationships between network structure and dynamics. In this
paper, we model in vitro cortical neuronal cultures as stochastic dynamical
systems and apply a method that reconstructs directed networks from dynamics
[Ching and Tam, Phys. Rev. E 95, 010301(R), 2017] to reveal directed effective
connectivity, namely the directed links and synaptic weights, of the neuronal
cultures from voltage measurements recorded by a multielectrode array. The
effective connectivity so obtained reproduces several features of cortical
regions in rats and monkeys and has similar network properties as the synaptic
network of the nematode C. elegans, the only organism whose entire nervous
system has been mapped out as of today. The distribution of the incoming degree
is bimodal and the distributions of the average incoming and outgoing synaptic
strength are non-Gaussian with long tails. The effective connectivity captures
different information from the commonly studied functional connectivity,
estimated using statistical correlation between spiking activities. The average
synaptic strengths of excitatory incoming and outgoing links are found to
increase with the spiking activity in the estimated effective connectivity but
not in the functional connectivity estimated using the same sets of voltage
measurements. These results thus demonstrate that the reconstructed effective
connectivity can capture the general properties of synaptic connections and
better reveal relationships between network structure and dynamics.
| [
{
"created": "Wed, 6 Apr 2022 11:15:42 GMT",
"version": "v1"
}
] | 2022-05-04 | [
[
"Sun",
"Chumin",
""
],
[
"Lin",
"K. C.",
""
],
[
"Yeung",
"C. Y.",
""
],
[
"Ching",
"Emily S. C.",
""
],
[
"Huang",
"Yu-Ting",
""
],
[
"Lai",
"Pik-Yin",
""
],
[
"Chan",
"C. K.",
""
]
] | In the study of biological networks, one of the major challenges is to understand the relationships between network structure and dynamics. In this paper, we model in vitro cortical neuronal cultures as stochastic dynamical systems and apply a method that reconstructs directed networks from dynamics [Ching and Tam, Phys. Rev. E 95, 010301(R), 2017] to reveal directed effective connectivity, namely the directed links and synaptic weights, of the neuronal cultures from voltage measurements recorded by a multielectrode array. The effective connectivity so obtained reproduces several features of cortical regions in rats and monkeys and has similar network properties as the synaptic network of the nematode C. elegans, the only organism whose entire nervous system has been mapped out as of today. The distribution of the incoming degree is bimodal and the distributions of the average incoming and outgoing synaptic strength are non-Gaussian with long tails. The effective connectivity captures different information from the commonly studied functional connectivity, estimated using statistical correlation between spiking activities. The average synaptic strengths of excitatory incoming and outgoing links are found to increase with the spiking activity in the estimated effective connectivity but not in the functional connectivity estimated using the same sets of voltage measurements. These results thus demonstrate that the reconstructed effective connectivity can capture the general properties of synaptic connections and better reveal relationships between network structure and dynamics. |
1610.01193 | Jing Xu | Jing Xu, Stephen J. King, Maryse Lapierre-Landry, Brian Nemec | Interplay between velocity and travel distance of kinesin-based
transport in the presence of tau | null | Biophysical Journal, 105, L23-5 (2013) | 10.1016/j.bpj.2013.10.006 | null | q-bio.BM q-bio.SC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although the disease-relevant microtubule-associated protein tau is known to
severely inhibit kinesin-based transport in vitro, the potential mechanisms for
reversing this detrimental effect to maintain healthy transport in cells remain
unknown. Here we report the unambiguous upregulation of multiple-kinesin travel
distance despite the presence of tau, via decreased single-kinesin velocity.
Interestingly, the presence of tau also modestly reduced cargo velocity in
multiple-kinesin transport, and our stochastic simulations indicate that the
tau-mediated reduction in single-kinesin travel underlies this observation.
Taken together, our observations highlight a nontrivial interplay between
velocity and travel distance for kinesin transport, and suggest that
single-kinesin velocity is a promising experimental handle for tuning the
effect of tau on multiple-kinesin travel distance.
| [
{
"created": "Tue, 4 Oct 2016 20:39:06 GMT",
"version": "v1"
}
] | 2017-07-26 | [
[
"Xu",
"Jing",
""
],
[
"King",
"Stephen J.",
""
],
[
"Lapierre-Landry",
"Maryse",
""
],
[
"Nemec",
"Brian",
""
]
] | Although the disease-relevant microtubule-associated protein tau is known to severely inhibit kinesin-based transport in vitro, the potential mechanisms for reversing this detrimental effect to maintain healthy transport in cells remain unknown. Here we report the unambiguous upregulation of multiple-kinesin travel distance despite the presence of tau, via decreased single-kinesin velocity. Interestingly, the presence of tau also modestly reduced cargo velocity in multiple-kinesin transport, and our stochastic simulations indicate that the tau-mediated reduction in single-kinesin travel underlies this observation. Taken together, our observations highlight a nontrivial interplay between velocity and travel distance for kinesin transport, and suggest that single-kinesin velocity is a promising experimental handle for tuning the effect of tau on multiple-kinesin travel distance. |
q-bio/0602004 | Illes Farkas | Balazs Adamcsek, Gergely Palla, Illes J. Farkas, Imre Derenyi, Tamas
Vicsek | CFinder: Locating cliques and overlapping modules in biological networks | The free academic research software, CFinder, used for the
publication is available at the website of the publication:
http://angel.elte.hu/clustering | Bioinformatics 22, 1021-1023 (2006) | 10.1093/bioinformatics/btl039 | null | q-bio.MN q-bio.GN | null | Summary: Most cellular tasks are performed not by individual proteins, but by
groups of functionally associated proteins, often referred to as modules. In a
protein assocation network modules appear as groups of densely interconnected
nodes, also called communities or clusters. These modules often overlap with
each other and form a network of their own, in which nodes (links) represent
the modules (overlaps). We introduce CFinder, a fast program locating and
visualizing overlapping, densely interconnected groups of nodes in undirected
graphs, and allowing the user to easily navigate between the original graph and
the web of these groups. We show that in gene (protein) association networks
CFinder can be used to predict the function(s) of a single protein and to
discover novel modules. CFinder is also very efficient for locating the cliques
of large sparse graphs.
Availability: CFinder (for Windows, Linux, and Macintosh) and its manual can
be downloaded from http://angel.elte.hu/clustering.
Contact: cfinder@angel.elte.hu
| [
{
"created": "Sat, 4 Feb 2006 10:20:25 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Adamcsek",
"Balazs",
""
],
[
"Palla",
"Gergely",
""
],
[
"Farkas",
"Illes J.",
""
],
[
"Derenyi",
"Imre",
""
],
[
"Vicsek",
"Tamas",
""
]
] | Summary: Most cellular tasks are performed not by individual proteins, but by groups of functionally associated proteins, often referred to as modules. In a protein assocation network modules appear as groups of densely interconnected nodes, also called communities or clusters. These modules often overlap with each other and form a network of their own, in which nodes (links) represent the modules (overlaps). We introduce CFinder, a fast program locating and visualizing overlapping, densely interconnected groups of nodes in undirected graphs, and allowing the user to easily navigate between the original graph and the web of these groups. We show that in gene (protein) association networks CFinder can be used to predict the function(s) of a single protein and to discover novel modules. CFinder is also very efficient for locating the cliques of large sparse graphs. Availability: CFinder (for Windows, Linux, and Macintosh) and its manual can be downloaded from http://angel.elte.hu/clustering. Contact: cfinder@angel.elte.hu |
0910.1577 | Rajesh Karmakar | Rajesh Karmakar | Conversion of graded to binary response in an activator-repressor system | 12 pages, Accepted for publication in Physical Review E | Phys. Rev. E 81, 021905 (2010) | 10.1103/PhysRevE.81.021905 | null | q-bio.MN cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Appropriate regulation of gene expression is essential to ensure that protein
synthesis occurs in a selective manner. The control of transcription is the
most dominant type of regulation mediated by a complex of molecules such as
transcription factors. In general, regulatory molecules are of two types:
activator and repressor. Activators promote the initiation of transcription
whereas repressors inhibit transcription. In many cases, they regulate the gene
transcription on binding the promoter mutually exclusively and the observed
gene expression response is either graded or binary. In experiments, the gene
expression response is quantified by the amount of proteins produced on varying
the concentration of an external inducer molecules in the cell. In this paper,
we study a gene regulatory network where activators and repressors both bind
the same promoter mutually exclusively. The network is modeled by assuming that
the gene can be in three possible states: repressed, unregulated and active. An
exact analytical expression for the steady-state probability distribution of
protein levels is then derived. The exact result helps to explain the
experimental observations that in the presence of activator molecules the
response is graded at all inducer levels whereas in the presence of both
activator and repressor molecules, the response is graded at low and high
inducer levels and binary at an intermediate inducer level.
| [
{
"created": "Thu, 8 Oct 2009 19:39:19 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Jan 2010 06:54:46 GMT",
"version": "v2"
}
] | 2010-02-04 | [
[
"Karmakar",
"Rajesh",
""
]
] | Appropriate regulation of gene expression is essential to ensure that protein synthesis occurs in a selective manner. The control of transcription is the most dominant type of regulation mediated by a complex of molecules such as transcription factors. In general, regulatory molecules are of two types: activator and repressor. Activators promote the initiation of transcription whereas repressors inhibit transcription. In many cases, they regulate the gene transcription on binding the promoter mutually exclusively and the observed gene expression response is either graded or binary. In experiments, the gene expression response is quantified by the amount of proteins produced on varying the concentration of an external inducer molecules in the cell. In this paper, we study a gene regulatory network where activators and repressors both bind the same promoter mutually exclusively. The network is modeled by assuming that the gene can be in three possible states: repressed, unregulated and active. An exact analytical expression for the steady-state probability distribution of protein levels is then derived. The exact result helps to explain the experimental observations that in the presence of activator molecules the response is graded at all inducer levels whereas in the presence of both activator and repressor molecules, the response is graded at low and high inducer levels and binary at an intermediate inducer level. |
2308.11665 | Cole Mathis | OoLEN (Origin of Life Early-career Network), Silke Asche, Carla
Bautista, David Boulesteix, Alexandre Champagne-Ruel, Cole Mathis, Omer
Markovitch, Zhen Peng, Alyssa Adams, Avinash Vicholous Dass, Arnaud Buch,
Eloi Camprubi, Enrico Sandro Colizzi, Stephanie Col\'on-Santos, Hannah
Dromiack, Valentina Erastova, Amanda Garcia, Ghjuvan Grimaud, Aaron Halpern,
Stuart A Harrison, Se\'an F. Jordan, Tony Z Jia, Amit Kahana, Artemy
Kolchinsky, Odin Moron-Garcia, Ryo Mizuuchi, Jingbo Nan, Yuliia Orlova, Ben
K. D. Pearce, Klaus Paschek, Martina Preiner, Silvana Pinna, Eduardo
Rodr\'iguez-Rom\'an, Loraine Schwander, Siddhant Sharma, Harrison B. Smith,
Andrey Vieira, Joana C. Xavier | What it takes to solve the Origin(s) of Life: An integrated review of
techniques | null | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Understanding the origin(s) of life (OoL) is a fundamental challenge for
science in the 21st century. Research on OoL spans many disciplines, including
chemistry, physics, biology, planetary sciences, computer science, mathematics
and philosophy. The sheer number of different scientific perspectives relevant
to the problem has resulted in the coexistence of diverse tools, techniques,
data, and software in OoL studies. This has made communication between the
disciplines relevant to the OoL extremely difficult because the interpretation
of data, analyses, or standards of evidence can vary dramatically. Here, we
hope to bridge this wide field of study by providing common ground via the
consolidation of tools and techniques rather than positing a unifying view on
how life emerges. We review the common tools and techniques that have been used
significantly in OoL studies in recent years. In particular, we aim to identify
which information is most relevant for comparing and integrating the results of
experimental analyses into mathematical and computational models. This review
aims to provide a baseline expectation and understanding of technical aspects
of origins research, rather than being a primer on any particular topic. As
such, it spans broadly -- from analytical chemistry to mathematical models --
and highlights areas of future work that will benefit from a multidisciplinary
approach to tackling the mystery of life's origin. Ultimately, we hope to
empower a new generation of OoL scientists by reviewing how they can
investigate life's origin, rather than dictating how to think about the
problem.
| [
{
"created": "Tue, 22 Aug 2023 04:46:19 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Aug 2023 23:12:27 GMT",
"version": "v2"
}
] | 2023-08-29 | [
[
"OoLEN",
"",
"",
"Origin of Life Early-career Network"
],
[
"Asche",
"Silke",
""
],
[
"Bautista",
"Carla",
""
],
[
"Boulesteix",
"David",
""
],
[
"Champagne-Ruel",
"Alexandre",
""
],
[
"Mathis",
"Cole",
""
],
[
... | Understanding the origin(s) of life (OoL) is a fundamental challenge for science in the 21st century. Research on OoL spans many disciplines, including chemistry, physics, biology, planetary sciences, computer science, mathematics and philosophy. The sheer number of different scientific perspectives relevant to the problem has resulted in the coexistence of diverse tools, techniques, data, and software in OoL studies. This has made communication between the disciplines relevant to the OoL extremely difficult because the interpretation of data, analyses, or standards of evidence can vary dramatically. Here, we hope to bridge this wide field of study by providing common ground via the consolidation of tools and techniques rather than positing a unifying view on how life emerges. We review the common tools and techniques that have been used significantly in OoL studies in recent years. In particular, we aim to identify which information is most relevant for comparing and integrating the results of experimental analyses into mathematical and computational models. This review aims to provide a baseline expectation and understanding of technical aspects of origins research, rather than being a primer on any particular topic. As such, it spans broadly -- from analytical chemistry to mathematical models -- and highlights areas of future work that will benefit from a multidisciplinary approach to tackling the mystery of life's origin. Ultimately, we hope to empower a new generation of OoL scientists by reviewing how they can investigate life's origin, rather than dictating how to think about the problem. |
0910.4915 | Per Arne Rikvold | Per Arne Rikvold (Florida State University) | Complex dynamics in coevolution models with ratio-dependent functional
response | 19 pages | Ecol. Complex. 6, 443-452 (2009) | 10.1016/j.ecocom.2009.08.007 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore the complex dynamical behavior of two simple predator-prey models
of biological coevolution that on the ecological level account for
interspecific and intraspecific competition, as well as adaptive foraging
behavior. The underlying individual-based population dynamics are based on a
ratio-dependent functional response [W.M. Getz, J. Theor. Biol. 108, 623
(1984)]. Analytical results for fixed-point population sizes in some simple
communities are derived and discussed. In long kinetic Monte Carlo simulations
we find quite robust, approximate 1/f noise in species diversity and population
sizes, as well as power-law distributions for the lifetimes of individual
species and the durations of periods of relative evolutionary stasis. Adaptive
foraging enhances coexistence of species and produces a metastable
low-diversity phase and a stable high-diversity phase.
| [
{
"created": "Mon, 26 Oct 2009 16:06:47 GMT",
"version": "v1"
}
] | 2009-11-28 | [
[
"Rikvold",
"Per Arne",
"",
"Florida State University"
]
] | We explore the complex dynamical behavior of two simple predator-prey models of biological coevolution that on the ecological level account for interspecific and intraspecific competition, as well as adaptive foraging behavior. The underlying individual-based population dynamics are based on a ratio-dependent functional response [W.M. Getz, J. Theor. Biol. 108, 623 (1984)]. Analytical results for fixed-point population sizes in some simple communities are derived and discussed. In long kinetic Monte Carlo simulations we find quite robust, approximate 1/f noise in species diversity and population sizes, as well as power-law distributions for the lifetimes of individual species and the durations of periods of relative evolutionary stasis. Adaptive foraging enhances coexistence of species and produces a metastable low-diversity phase and a stable high-diversity phase. |
1603.02062 | Erich Schmid | Erich W. Schmid | Extracellular stimulation of nerve cells with electric current spikes
induced by voltage steps | 17 pages, 9 figuress | null | null | null | q-bio.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A new stimulation paradigm is presented for the stimulation of nerve cells by
extracellular electric currents. In the new paradigm stimulation is achieved
with the current spike induced by a voltage step whenever the voltage step is
applied to a live biological tissue. By experimental evidence and theoretical
arguments, it is shown that this spike is well suited for the stimulation of
nerve cells. Stimulation of the human tongue is used for proof of principle.
Charge injection thresholds are measured for various voltages. The time-profile
of the current spike used in the experiment has a half-width of about 1
microsecond. The decay of the spike is non-exponential. The spike has at least
three distinctly different phases. A Maxwell phase is followed by a
charge-rearrangement phase. Charging of cell membranes is completed in a third
phase. All three phases contribute to depolarization or hyperpolarization of
cell membranes. Due to the short duration of the spike the charge transfer is
very small. The activation time (time of no return) of nerve cell membranes
leading to an action potential is measured and found to be unexpectedly short.
It can become as short as 3 microseconds for a voltage step of 10 V or higher.
| [
{
"created": "Sat, 27 Feb 2016 15:22:33 GMT",
"version": "v1"
}
] | 2016-03-08 | [
[
"Schmid",
"Erich W.",
""
]
] | A new stimulation paradigm is presented for the stimulation of nerve cells by extracellular electric currents. In the new paradigm stimulation is achieved with the current spike induced by a voltage step whenever the voltage step is applied to a live biological tissue. By experimental evidence and theoretical arguments, it is shown that this spike is well suited for the stimulation of nerve cells. Stimulation of the human tongue is used for proof of principle. Charge injection thresholds are measured for various voltages. The time-profile of the current spike used in the experiment has a half-width of about 1 microsecond. The decay of the spike is non-exponential. The spike has at least three distinctly different phases. A Maxwell phase is followed by a charge-rearrangement phase. Charging of cell membranes is completed in a third phase. All three phases contribute to depolarization or hyperpolarization of cell membranes. Due to the short duration of the spike the charge transfer is very small. The activation time (time of no return) of nerve cell membranes leading to an action potential is measured and found to be unexpectedly short. It can become as short as 3 microseconds for a voltage step of 10 V or higher. |
1404.6668 | Sayak Mukherjee | Sayak Mukherjee, Kristin E. Weimer, Sang-Cheol Seok, Will C. Ray, C.
Jayaprakash, Veronica J. Vieland, W. Edward Swords, Jayajit Das | Host-to-host variation of ecological interactions in polymicrobial
infections | 39 Pages 6 figures | null | 10.1088/1478-3975/12/1/016003 | null | q-bio.PE cond-mat.stat-mech | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Host-to-host variability with respect to interactions between microorganisms
and multicellular hosts are commonly observed in infection and in homeostasis.
However, the majority of mechanistic models used in analyzing
host-microorganism relationships, as well as most of the ecological theories
proposed to explain co-evolution of host and microbes, are based on averages
across a host population. By assuming that observed variations are random and
independent, these models overlook the role of inter-host differences. Here we
analyze mechanisms underlying host-to-host variations, using the
well-characterized experimental infection model of polymicrobial otitis media
(OM) in chinchillas, in combination with population dynamic models and a
Maximum Entropy (MaxEnt) based inference scheme. We find that the nature of the
interactions among bacterial species critically regulates host-to-host
variations of these interactions. Surprisingly, seemingly unrelated phenomena,
such as the efficiency of individual bacterial species in utilizing nutrients
for growth and the microbe-specific host immune response, can become
interdependent in a host population. The latter finding suggests a potential
mechanism that could lead to selection of specific strains of bacterial species
during the coevolution of the host immune response and the bacterial species.
| [
{
"created": "Sat, 26 Apr 2014 18:38:30 GMT",
"version": "v1"
}
] | 2015-06-19 | [
[
"Mukherjee",
"Sayak",
""
],
[
"Weimer",
"Kristin E.",
""
],
[
"Seok",
"Sang-Cheol",
""
],
[
"Ray",
"Will C.",
""
],
[
"Jayaprakash",
"C.",
""
],
[
"Vieland",
"Veronica J.",
""
],
[
"Swords",
"W. Edward",
""... | Host-to-host variability with respect to interactions between microorganisms and multicellular hosts are commonly observed in infection and in homeostasis. However, the majority of mechanistic models used in analyzing host-microorganism relationships, as well as most of the ecological theories proposed to explain co-evolution of host and microbes, are based on averages across a host population. By assuming that observed variations are random and independent, these models overlook the role of inter-host differences. Here we analyze mechanisms underlying host-to-host variations, using the well-characterized experimental infection model of polymicrobial otitis media (OM) in chinchillas, in combination with population dynamic models and a Maximum Entropy (MaxEnt) based inference scheme. We find that the nature of the interactions among bacterial species critically regulates host-to-host variations of these interactions. Surprisingly, seemingly unrelated phenomena, such as the efficiency of individual bacterial species in utilizing nutrients for growth and the microbe-specific host immune response, can become interdependent in a host population. The latter finding suggests a potential mechanism that could lead to selection of specific strains of bacterial species during the coevolution of the host immune response and the bacterial species. |
q-bio/0407042 | Francisco Guinea | F. Guinea, V. A. A. Jansen, N. Stollenwerk | Statistics of infections with diversity in the pathogenicity | null | null | null | null | q-bio.PE cond-mat.stat-mech q-bio.QM | null | The statistics of outbreaks in a model for the propagation of meningococcal
diseases is analyzed, taking into account the possibility that the population
is fragmented into weakly connected patches. It is shown that, depending on the
size of of the sample studied, the ratio between the variance and the mean of
infected cases can vary from one (Poisson statistics) to the inverse of the
infection rate.
| [
{
"created": "Fri, 30 Jul 2004 12:27:53 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Guinea",
"F.",
""
],
[
"Jansen",
"V. A. A.",
""
],
[
"Stollenwerk",
"N.",
""
]
] | The statistics of outbreaks in a model for the propagation of meningococcal diseases is analyzed, taking into account the possibility that the population is fragmented into weakly connected patches. It is shown that, depending on the size of of the sample studied, the ratio between the variance and the mean of infected cases can vary from one (Poisson statistics) to the inverse of the infection rate. |
2406.14842 | Hui Ma | Hui Ma and Kai Chen | Online t-SNE for single-cell RNA-seq | null | null | null | null | q-bio.GN cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to the sequential sample arrival, changing experiment conditions, and
evolution of knowledge, the demand to continually visualize evolving structures
of sequential and diverse single-cell RNA-sequencing (scRNA-seq) data becomes
indispensable. However, as one of the state-of-the-art visualization and
analysis methods for scRNA-seq, t-distributed stochastic neighbor embedding
(t-SNE) merely visualizes static scRNA-seq data offline and fails to meet the
demand well. To address these challenges, we introduce online t-SNE to
seamlessly integrate sequential scRNA-seq data. Online t-SNE achieves this by
leveraging the embedding space of old samples, exploring the embedding space of
new samples, and aligning the two embedding spaces on the fly. Consequently,
online t-SNE dramatically enables the continual discovery of new structures and
high-quality visualization of new scRNA-seq data without retraining from
scratch. We showcase the formidable visualization capabilities of online t-SNE
across diverse sequential scRNA-seq datasets.
| [
{
"created": "Fri, 21 Jun 2024 03:02:45 GMT",
"version": "v1"
}
] | 2024-06-24 | [
[
"Ma",
"Hui",
""
],
[
"Chen",
"Kai",
""
]
] | Due to the sequential sample arrival, changing experiment conditions, and evolution of knowledge, the demand to continually visualize evolving structures of sequential and diverse single-cell RNA-sequencing (scRNA-seq) data becomes indispensable. However, as one of the state-of-the-art visualization and analysis methods for scRNA-seq, t-distributed stochastic neighbor embedding (t-SNE) merely visualizes static scRNA-seq data offline and fails to meet the demand well. To address these challenges, we introduce online t-SNE to seamlessly integrate sequential scRNA-seq data. Online t-SNE achieves this by leveraging the embedding space of old samples, exploring the embedding space of new samples, and aligning the two embedding spaces on the fly. Consequently, online t-SNE dramatically enables the continual discovery of new structures and high-quality visualization of new scRNA-seq data without retraining from scratch. We showcase the formidable visualization capabilities of online t-SNE across diverse sequential scRNA-seq datasets. |
2404.15387 | Alan Inglis | Alan Inglis, Andrew Parnell, Natarajan Subramani, Fiona Doohan | Machine Learning Applied to the Detection of Mycotoxin in Food: A Review | 39 pages, 8 figures, review paper | null | null | null | q-bio.QM cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mycotoxins, toxic secondary metabolites produced by certain fungi, pose
significant threats to global food safety and public health. These compounds
can contaminate a variety of crops, leading to economic losses and health risks
to both humans and animals. Traditional lab analysis methods for mycotoxin
detection can be time-consuming and may not always be suitable for large-scale
screenings. However, in recent years, machine learning (ML) methods have gained
popularity for use in the detection of mycotoxins and in the food safety
industry in general, due to their accurate and timely predictions. We provide a
systematic review on some of the recent ML applications for
detecting/predicting the presence of mycotoxin on a variety of food
ingredients, highlighting their advantages, challenges, and potential for
future advancements. We address the need for reproducibility and transparency
in ML research through open access to data and code. An observation from our
findings is the frequent lack of detailed reporting on hyperparameters in many
studies as well as a lack of open source code, which raises concerns about the
reproducibility and optimisation of the ML models used. The findings reveal
that while the majority of studies predominantly utilised neural networks for
mycotoxin detection, there was a notable diversity in the types of neural
network architectures employed, with convolutional neural networks being the
most popular.
| [
{
"created": "Tue, 23 Apr 2024 14:13:31 GMT",
"version": "v1"
}
] | 2024-04-25 | [
[
"Inglis",
"Alan",
""
],
[
"Parnell",
"Andrew",
""
],
[
"Subramani",
"Natarajan",
""
],
[
"Doohan",
"Fiona",
""
]
] | Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general, due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies as well as a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular. |
1404.4548 | Jonathan Potts | Jonathan R. Potts, Mark A. Lewis | A mathematical approach to territorial pattern formation | Note: this is a pre-print version and may contain small errors.
Please see the published version if possible | The American Mathematical Monthly (2014) 121(9):754-770 | 10.4169/amer.math.monthly.121.09.754 | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Territorial behaviour is widespread in the animal kingdom, with creatures
seeking to gain parts of space for their exclusive use. It arises through a
complicated interplay of many different behavioural features. Extracting and
quantifying the processes that give rise to territorial patterns requires both
mathematical models of movement and interaction mechanisms, together with
statistical techniques for rigorously extracting parameters from data. Here, we
give a brisk, pedagogical overview of the techniques so far developed to tackle
the problem of territory formation. We give some examples of what has already
been achieved using these techniques, together with pointers as to where we
believe the future lies in this area of study. This progress is a single
example of a major aim for 21st century science: to construct quantitatively
predictive theory for ecological systems.
| [
{
"created": "Thu, 17 Apr 2014 14:57:07 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Jan 2015 07:30:36 GMT",
"version": "v2"
}
] | 2015-01-26 | [
[
"Potts",
"Jonathan R.",
""
],
[
"Lewis",
"Mark A.",
""
]
] | Territorial behaviour is widespread in the animal kingdom, with creatures seeking to gain parts of space for their exclusive use. It arises through a complicated interplay of many different behavioural features. Extracting and quantifying the processes that give rise to territorial patterns requires both mathematical models of movement and interaction mechanisms, together with statistical techniques for rigorously extracting parameters from data. Here, we give a brisk, pedagogical overview of the techniques so far developed to tackle the problem of territory formation. We give some examples of what has already been achieved using these techniques, together with pointers as to where we believe the future lies in this area of study. This progress is a single example of a major aim for 21st century science: to construct quantitatively predictive theory for ecological systems. |
2407.15028 | Roel Ceballos | Julienne Kate N. Kintanar, Roel F. Ceballos | Statistical Models for Outbreak Detection of Measles in North Cotabato,
Philippines | null | Mindanao Journal of Science and Technology, 22(1) (2024) | null | null | q-bio.QM stat.AP stat.CO stat.ME | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A measles outbreak occurs when the number of cases of measles in the
population exceeds the typical level. Outbreaks that are not detected and
managed early can increase mortality and morbidity and incur costs from
activities responding to these events. The number of measles cases in the
Province of North Cotabato, Philippines, was used in this study. Weekly
reported cases of measles from January 2016 to December 2021 were provided by
the Epidemiology and Surveillance Unit of the North Cotabato Provincial Health
Office. Several integer-valued autoregressive (INAR) time series models were
used to explore the possibility of detecting and identifying measles outbreaks
in the province along with the classical ARIMA model. These models were
evaluated based on goodness of fit, measles outbreak detection accuracy, and
timeliness. The results of this study confirmed that INAR models have the
conceptual advantage over ARIMA since the latter produces non-integer
forecasts, which are not realistic for count data such as measles cases. Among
the INAR models, the ZINGINAR (1) model was recommended for having a good model
fit and timely and accurate detection of outbreaks. Furthermore, policymakers
and decision-makers from relevant government agencies can use the ZINGINAR (1)
model to improve disease surveillance and implement preventive measures against
contagious diseases beforehand.
| [
{
"created": "Sun, 21 Jul 2024 01:25:51 GMT",
"version": "v1"
}
] | 2024-07-23 | [
[
"Kintanar",
"Julienne Kate N.",
""
],
[
"Ceballos",
"Roel F.",
""
]
] | A measles outbreak occurs when the number of cases of measles in the population exceeds the typical level. Outbreaks that are not detected and managed early can increase mortality and morbidity and incur costs from activities responding to these events. The number of measles cases in the Province of North Cotabato, Philippines, was used in this study. Weekly reported cases of measles from January 2016 to December 2021 were provided by the Epidemiology and Surveillance Unit of the North Cotabato Provincial Health Office. Several integer-valued autoregressive (INAR) time series models were used to explore the possibility of detecting and identifying measles outbreaks in the province along with the classical ARIMA model. These models were evaluated based on goodness of fit, measles outbreak detection accuracy, and timeliness. The results of this study confirmed that INAR models have the conceptual advantage over ARIMA since the latter produces non-integer forecasts, which are not realistic for count data such as measles cases. Among the INAR models, the ZINGINAR (1) model was recommended for having a good model fit and timely and accurate detection of outbreaks. Furthermore, policymakers and decision-makers from relevant government agencies can use the ZINGINAR (1) model to improve disease surveillance and implement preventive measures against contagious diseases beforehand. |
1504.07382 | Francois Saint-Antonin | Francois Saint-Antonin | Reply to Mills et al.: Oceanic Anoxic Event, a mechanism for selecting
animals with the ability to survive hypoxic conditions | null | null | null | null | q-bio.PE | http://creativecommons.org/licenses/by/3.0/ | It is generally considered that animal life was triggered by the rise of
oxygen levels. Based on experiments evaluating the minimum range of oxygen
levels at which sponges can survive, Mills and coauthors
(doi:10.1073/pnas.1400547111) defend the opposite view. However, the authors do
not demonstrate that "animal life was not triggered by the oxygen rise" is the
only possible and unique conclusion from their observation. In this reply, it
is suggested that a mechanism to explain the ability of sponges to survive at
low oxygen biota is Ocean Anoxic Events. These lead to oxygen depletion and a
series of them would selectively favor animals able to survive at low oxygen
levels. Thus, the origin of the ability of marine animals to survive in low
oxygen biota remains to be clarified.
| [
{
"created": "Tue, 28 Apr 2015 08:52:06 GMT",
"version": "v1"
}
] | 2015-04-29 | [
[
"Saint-Antonin",
"Francois",
""
]
] | It is generally considered that animal life was triggered by the rise of oxygen levels. Based on experiments evaluating the minimum range of oxygen levels at which sponges can survive, Mills and coauthors (doi:10.1073/pnas.1400547111) defend the opposite view. However, the authors do not demonstrate that "animal life was not triggered by the oxygen rise" is the only possible and unique conclusion from their observation. In this reply, it is suggested that a mechanism to explain the ability of sponges to survive at low oxygen biota is Ocean Anoxic Events. These lead to oxygen depletion and a series of them would selectively favor animals able to survive at low oxygen levels. Thus, the origin of the ability of marine animals to survive in low oxygen biota remains to be clarified. |
1601.07041 | Ciprian Palaghianu Dr. | Ciprian Palaghianu, Marian Dragoi | Patterns of mast fruiting - a stochastic approach | 6 oages, 3 figures | Journal of Landscape Management, 6 (2), 56-61 (2015) | null | null | q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mast fruiting represents a synchronous population behaviour which can spread
on large landscape areas. This reproductive pattern is generally perceived as a
synchronous periodic production of large seed crops and has a significant
practical importance to forest natural regeneration in order to synchronize
cuttings. The mechanisms of masting are still argued and models of this
phenomenon are uncommon, so a stochastic approach can cast significant light on
some particular aspects. Trees manage to get synchronized and coordinate their
reproductive routines. But is it possible that trees get synchronized by
chance, absolutely random? Using a Monte Carlo simulation of seeding years and
a theoretical masting pattern, a stochastic analysis is performed in order to
assess the chance of random mast fruiting. Two populations of 100 trees, with
different fruiting periodicity of 2-3 years and 4-6 years, were set and the
fruition dynamic was simulated for 100 years. The results show that periodicity
itself cannot induce by chance the masting effect, but periodicity
mathematically influences the reproductive pattern.
| [
{
"created": "Sun, 24 Jan 2016 10:27:46 GMT",
"version": "v1"
}
] | 2016-02-12 | [
[
"Palaghianu",
"Ciprian",
""
],
[
"Dragoi",
"Marian",
""
]
] | Mast fruiting represents a synchronous population behaviour which can spread on large landscape areas. This reproductive pattern is generally perceived as a synchronous periodic production of large seed crops and has a significant practical importance to forest natural regeneration in order to synchronize cuttings. The mechanisms of masting are still argued and models of this phenomenon are uncommon, so a stochastic approach can cast significant light on some particular aspects. Trees manage to get synchronized and coordinate their reproductive routines. But is it possible that trees get synchronized by chance, absolutely random? Using a Monte Carlo simulation of seeding years and a theoretical masting pattern, a stochastic analysis is performed in order to assess the chance of random mast fruiting. Two populations of 100 trees, with different fruiting periodicity of 2-3 years and 4-6 years, were set and the fruition dynamic was simulated for 100 years. The results show that periodicity itself cannot induce by chance the masting effect, but periodicity mathematically influences the reproductive pattern. |
0710.3944 | Swanand Gore | Swanand Gore and Tom Blundell | Crystallographic modelling of protein loops and their heterogeneity with
Rappertk | null | null | null | null | q-bio.BM | null | Background. All-atom crystallographic refinement of proteins is a laborious
manually driven procedure, as a result of which, alternative and multiconformer
interpretations are not routinely investigated.
Results. We describe efficient loop sampling procedures in Rappertk and
demonstrate that single loops in proteins can be automatically and accurately
modelled with few positional restraints. Loops constructed with a composite
CNS/Rappertk protocol consistently have better Rfree than those with CNS alone.
This approach is extended to a more realistic scenario where there are often
large positional uncertainties in loops along with small imperfections in the
secondary structural framework. Both ensemble and collection methods are used
to estimate the structural heterogeneity of loop regions.
Conclusion. Apart from benchmarking Rappertk for the all-atom protein
refinement task, this work also demonstrates its utility in both aspects of
loop modelling - building a single conformer and estimating structural
heterogeneity the loops can exhibit.
| [
{
"created": "Sun, 21 Oct 2007 15:42:20 GMT",
"version": "v1"
}
] | 2007-10-23 | [
[
"Gore",
"Swanand",
""
],
[
"Blundell",
"Tom",
""
]
] | Background. All-atom crystallographic refinement of proteins is a laborious manually driven procedure, as a result of which, alternative and multiconformer interpretations are not routinely investigated. Results. We describe efficient loop sampling procedures in Rappertk and demonstrate that single loops in proteins can be automatically and accurately modelled with few positional restraints. Loops constructed with a composite CNS/Rappertk protocol consistently have better Rfree than those with CNS alone. This approach is extended to a more realistic scenario where there are often large positional uncertainties in loops along with small imperfections in the secondary structural framework. Both ensemble and collection methods are used to estimate the structural heterogeneity of loop regions. Conclusion. Apart from benchmarking Rappertk for the all-atom protein refinement task, this work also demonstrates its utility in both aspects of loop modelling - building a single conformer and estimating structural heterogeneity the loops can exhibit. |
2103.01307 | Vasily Romanchak | Vasily Romanchak | About solving the Fechner-Stevens problem | 7 pages | null | 10.13140/RG.2.2.16725.76002 | null | q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we prove that the Fechner and Stevens laws are equivalent
(coincide up to isomorphism). Therefore, the problem does not exist.
| [
{
"created": "Fri, 26 Feb 2021 14:49:49 GMT",
"version": "v1"
}
] | 2021-03-03 | [
[
"Romanchak",
"Vasily",
""
]
] | In this paper, we prove that the Fechner and Stevens laws are equivalent (coincide up to isomorphism). Therefore, the problem does not exist. |
1711.04495 | Chiranjib Patra MR | Chiranjib Patra | Geo-spatial Monitoring Of Infectious Diseases By Unmanned Aerial
Vehicles | This paper was presented at GeoMundus 2017
(http://www.geomundus.org/2017/) and was one of the winners of Travel Grant
for the presentation of Abstract at the Institute for GeoInformatics ,
Munster, Germany | null | null | null | q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent development in unmanned UAV technology paved the way for numerous
applications in diverse cross discipline fields. One of the main feature of UAV
s are their portability in terms of size that allows them to navigate through
fairly hostile environments and collect data . This data collection leads to
the interpretation of the behavior and predictability according to the analysis
as presented by data science. The application of UAV to monitor the population
and climate geography is well documented. But the usage of UAV to study the
germs in the atmosphere is not well documented or absent. As air remains one of
main medium of transmission of germs so there must be some kind of signature
specific for a particular kind of germ. Using this as cue in this present
communication a hypothetical model to study the spread of disease is presented.
This model can help the epidemiologists to understand the mechanism of
microbial traffic like for example flu getting transferred within the same
species or cross species ,spatial diffusion like for example human traveling
pattern and newly recognized diseases for example various type of flu and
vector borne diseases like malaria , dengue etc. This model also covers some
relevant scenarios like global climate change, political ecologic emergences of
aerial transmitted diseases.
| [
{
"created": "Mon, 13 Nov 2017 10:02:42 GMT",
"version": "v1"
}
] | 2017-11-15 | [
[
"Patra",
"Chiranjib",
""
]
] | Recent development in unmanned UAV technology paved the way for numerous applications in diverse cross discipline fields. One of the main feature of UAV s are their portability in terms of size that allows them to navigate through fairly hostile environments and collect data . This data collection leads to the interpretation of the behavior and predictability according to the analysis as presented by data science. The application of UAV to monitor the population and climate geography is well documented. But the usage of UAV to study the germs in the atmosphere is not well documented or absent. As air remains one of main medium of transmission of germs so there must be some kind of signature specific for a particular kind of germ. Using this as cue in this present communication a hypothetical model to study the spread of disease is presented. This model can help the epidemiologists to understand the mechanism of microbial traffic like for example flu getting transferred within the same species or cross species ,spatial diffusion like for example human traveling pattern and newly recognized diseases for example various type of flu and vector borne diseases like malaria , dengue etc. This model also covers some relevant scenarios like global climate change, political ecologic emergences of aerial transmitted diseases. |
q-bio/0703018 | Greg Stephens | Luis M. A. Bettencourt, Greg J. Stephens, Michael I. Ham, and Guenter
W. Gross | The functional structure of cortical neuronal networks grown in vitro | 12 pages, 5 figures | Phys. Rev. E 75, 021915 (2007) | 10.1103/PhysRevE.75.021915 | LAUR-06-5040 | q-bio.NC cond-mat.dis-nn | null | We apply an information theoretic treatment of action potential time series
measured with microelectrode arrays to estimate the connectivity of mammalian
neuronal cell assemblies grown {\it in vitro}. We infer connectivity between
two neurons via the measurement of the mutual information between their spike
trains. In addition we measure higher point multi-informations between any two
spike trains conditional on the activity of a third cell, as a means to
identify and distinguish classes of functional connectivity among three
neurons. The use of a conditional three-cell measure removes some
interpretational shortcomings of the pairwise mutual information and sheds
light into the functional connectivity arrangements of any three cells. We
analyze the resultant connectivity graphs in light of other complex networks
and demonstrate that, despite their {\it ex vivo} development, the connectivity
maps derived from cultured neural assemblies are similar to other biological
networks and display nontrivial structure in clustering coefficient, network
diameter and assortative mixing. Specifically we show that these networks are
weakly disassortative small world graphs, which differ significantly in their
structure from randomized graphs with the same degree. We expect our analysis
to be useful in identifying the computational motifs of a wide variety of
complex networks, derived from time series data.
| [
{
"created": "Wed, 7 Mar 2007 16:08:52 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Bettencourt",
"Luis M. A.",
""
],
[
"Stephens",
"Greg J.",
""
],
[
"Ham",
"Michael I.",
""
],
[
"Gross",
"Guenter W.",
""
]
] | We apply an information theoretic treatment of action potential time series measured with microelectrode arrays to estimate the connectivity of mammalian neuronal cell assemblies grown {\it in vitro}. We infer connectivity between two neurons via the measurement of the mutual information between their spike trains. In addition we measure higher point multi-informations between any two spike trains conditional on the activity of a third cell, as a means to identify and distinguish classes of functional connectivity among three neurons. The use of a conditional three-cell measure removes some interpretational shortcomings of the pairwise mutual information and sheds light into the functional connectivity arrangements of any three cells. We analyze the resultant connectivity graphs in light of other complex networks and demonstrate that, despite their {\it ex vivo} development, the connectivity maps derived from cultured neural assemblies are similar to other biological networks and display nontrivial structure in clustering coefficient, network diameter and assortative mixing. Specifically we show that these networks are weakly disassortative small world graphs, which differ significantly in their structure from randomized graphs with the same degree. We expect our analysis to be useful in identifying the computational motifs of a wide variety of complex networks, derived from time series data. |
2103.10166 | Alessandro Lameiras Koerich | Bernardo B. Gatto, Juan G. Colonna, Eulanda M. dos Santos, Alessandro
L. Koerich, Kazuhiro Fukui | Discriminative Singular Spectrum Classifier with Applications on
Bioacoustic Signal Recognition | 15 pages | null | null | null | q-bio.QM cs.LG cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic analysis of bioacoustic signals is a fundamental tool to evaluate
the vitality of our planet. Frogs and bees, for instance, may act like
biological sensors providing information about environmental changes. This task
is fundamental for ecological monitoring still includes many challenges such as
nonuniform signal length processing, degraded target signal due to
environmental noise, and the scarcity of the labeled samples for training
machine learning. To tackle these challenges, we present a bioacoustic signal
classifier equipped with a discriminative mechanism to extract useful features
for analysis and classification efficiently. The proposed classifier does not
require a large amount of training data and handles nonuniform signal length
natively. Unlike current bioacoustic recognition methods, which are
task-oriented, the proposed model relies on transforming the input signals into
vector subspaces generated by applying Singular Spectrum Analysis (SSA). Then,
a subspace is designed to expose discriminative features. The proposed model
shares end-to-end capabilities, which is desirable in modern machine learning
systems. This formulation provides a segmentation-free and noise-tolerant
approach to represent and classify bioacoustic signals and a highly compact
signal descriptor inherited from SSA. The validity of the proposed method is
verified using three challenging bioacoustic datasets containing anuran, bee,
and mosquito species. Experimental results on three bioacoustic datasets have
shown the competitive performance of the proposed method compared to commonly
employed methods for bioacoustics signal classification in terms of accuracy.
| [
{
"created": "Thu, 18 Mar 2021 11:01:21 GMT",
"version": "v1"
}
] | 2021-03-19 | [
[
"Gatto",
"Bernardo B.",
""
],
[
"Colonna",
"Juan G.",
""
],
[
"Santos",
"Eulanda M. dos",
""
],
[
"Koerich",
"Alessandro L.",
""
],
[
"Fukui",
"Kazuhiro",
""
]
] | Automatic analysis of bioacoustic signals is a fundamental tool to evaluate the vitality of our planet. Frogs and bees, for instance, may act like biological sensors providing information about environmental changes. This task is fundamental for ecological monitoring still includes many challenges such as nonuniform signal length processing, degraded target signal due to environmental noise, and the scarcity of the labeled samples for training machine learning. To tackle these challenges, we present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. The proposed classifier does not require a large amount of training data and handles nonuniform signal length natively. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces generated by applying Singular Spectrum Analysis (SSA). Then, a subspace is designed to expose discriminative features. The proposed model shares end-to-end capabilities, which is desirable in modern machine learning systems. This formulation provides a segmentation-free and noise-tolerant approach to represent and classify bioacoustic signals and a highly compact signal descriptor inherited from SSA. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species. Experimental results on three bioacoustic datasets have shown the competitive performance of the proposed method compared to commonly employed methods for bioacoustics signal classification in terms of accuracy. |
1901.05315 | Divine Wanduku | Divine Wanduku | On a family of stochastic SVIR influenza epidemic models and maximum
likelihood estimation | Math. Model. in Health, Social and Appl. Sci., Springer | null | 10.1007/978-981-15-2286-4_2 | null | q-bio.PE stat.ME | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This study presents a family of stochastic models for the dynamics of
influenza in a closed human population. We consider treatment for the disease
in the form of vaccination, and incorporate the periods of effectiveness of the
vaccine and infectiousness for the individuals in the population. Our model is
a SVIR model, with trinomial transition probabilities, where all individuals
who recover from the disease acquire permanent natural immunity against the
strain of the disease. Special SVIR models in the family are presented, based
on the structure of the probability of getting infection and vaccination at any
instant. The methods of maximum likelihood, and expectation maximization are
derived for the parameters of the chain. Moreover, estimators for some
epidemiological assessment parameters, such as the basic reproduction number
are computed. Numerical simulation examples are presented for the model.
| [
{
"created": "Tue, 15 Jan 2019 16:15:03 GMT",
"version": "v1"
}
] | 2020-05-05 | [
[
"Wanduku",
"Divine",
""
]
] | This study presents a family of stochastic models for the dynamics of influenza in a closed human population. We consider treatment for the disease in the form of vaccination, and incorporate the periods of effectiveness of the vaccine and infectiousness for the individuals in the population. Our model is a SVIR model, with trinomial transition probabilities, where all individuals who recover from the disease acquire permanent natural immunity against the strain of the disease. Special SVIR models in the family are presented, based on the structure of the probability of getting infection and vaccination at any instant. The methods of maximum likelihood, and expectation maximization are derived for the parameters of the chain. Moreover, estimators for some epidemiological assessment parameters, such as the basic reproduction number are computed. Numerical simulation examples are presented for the model. |
1706.04117 | Khaled Sayed | Khaled Sayed, Cheryl A. Telmer, Adam A. Butchy, and Natasa
Miskov-Zivanov | Recipes for Translating Big Data Machine Reading to Executable Cellular
Signaling Models | null | null | null | null | q-bio.MN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the tremendous increase in the amount of biological literature,
developing automated methods for extracting big data from papers, building
models and explaining big mechanisms becomes a necessity. We describe here our
approach to translating machine reading outputs, obtained by reading bio-
logical signaling literature, to discrete models of cellular networks. We use
out- puts from three different reading engines, and describe our approach to
translating their different features, using examples from reading cancer
literature. We also outline several issues that still arise when assembling
cellular network models from state-of-the-art reading engines. Finally, we
illustrate the details of our approach with a case study in pancreatic cancer.
| [
{
"created": "Tue, 13 Jun 2017 15:21:22 GMT",
"version": "v1"
}
] | 2017-06-14 | [
[
"Sayed",
"Khaled",
""
],
[
"Telmer",
"Cheryl A.",
""
],
[
"Butchy",
"Adam A.",
""
],
[
"Miskov-Zivanov",
"Natasa",
""
]
] | With the tremendous increase in the amount of biological literature, developing automated methods for extracting big data from papers, building models and explaining big mechanisms becomes a necessity. We describe here our approach to translating machine reading outputs, obtained by reading bio- logical signaling literature, to discrete models of cellular networks. We use out- puts from three different reading engines, and describe our approach to translating their different features, using examples from reading cancer literature. We also outline several issues that still arise when assembling cellular network models from state-of-the-art reading engines. Finally, we illustrate the details of our approach with a case study in pancreatic cancer. |
1507.02148 | Lu Xie | Lu Xie, Gregory R. Smith, Russell Schwartz | Derivative-free optimization of rate parameters of capsid assembly
models from bulk in vitro data | null | null | null | null | q-bio.QM physics.bio-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The assembly of virus capsids from free coat proteins proceeds by a
complicated cascade of association and dissociation steps, the great majority
of which cannot be directly experimentally observed. This has made capsid
assembly a rich field for computational models to attempt to fill the gaps in
what is experimentally observable. Nonetheless, accurate simulation predictions
depend on accurate models and there are substantial obstacles to model
inference for such systems. Here, we describe progress in learning parameters
for capsid assembly systems, particularly kinetic rate constants of coat-coat
interactions, by computationally fitting simulations to experimental data. We
previously developed an approach to learn rate parameters of coat-coat
interactions by minimizing the deviation between real and simulated light
scattering data monitoring bulk capsid assembly in vitro. This is a difficult
data-fitting problem, however, because of the high computational cost of
simulating assembly trajectories, the stochastic noise inherent to the models,
and the limited and noisy data available for fitting. Here we show that a newer
classes of methods, based on derivative-free optimization (DFO), can more
quickly and precisely learn physical parameters from static light scattering
data. We further explore how the advantages of the approaches might be affected
by alternative data sources through simulation of a model of time-resolved mass
spectrometry data, an alternative technology for monitoring bulk capsid
assembly that can be expected to provide much richer data. The results show
that advances in both the data and the algorithms can improve model inference,
with rich data leading to high-quality fits for all methods, but DFO methods
showing substantial advantages over less informative data sources better
representative of the current experimental practice.
| [
{
"created": "Tue, 7 Jul 2015 18:09:47 GMT",
"version": "v1"
}
] | 2015-07-09 | [
[
"Xie",
"Lu",
""
],
[
"Smith",
"Gregory R.",
""
],
[
"Schwartz",
"Russell",
""
]
] | The assembly of virus capsids from free coat proteins proceeds by a complicated cascade of association and dissociation steps, the great majority of which cannot be directly experimentally observed. This has made capsid assembly a rich field for computational models to attempt to fill the gaps in what is experimentally observable. Nonetheless, accurate simulation predictions depend on accurate models and there are substantial obstacles to model inference for such systems. Here, we describe progress in learning parameters for capsid assembly systems, particularly kinetic rate constants of coat-coat interactions, by computationally fitting simulations to experimental data. We previously developed an approach to learn rate parameters of coat-coat interactions by minimizing the deviation between real and simulated light scattering data monitoring bulk capsid assembly in vitro. This is a difficult data-fitting problem, however, because of the high computational cost of simulating assembly trajectories, the stochastic noise inherent to the models, and the limited and noisy data available for fitting. Here we show that a newer classes of methods, based on derivative-free optimization (DFO), can more quickly and precisely learn physical parameters from static light scattering data. We further explore how the advantages of the approaches might be affected by alternative data sources through simulation of a model of time-resolved mass spectrometry data, an alternative technology for monitoring bulk capsid assembly that can be expected to provide much richer data. The results show that advances in both the data and the algorithms can improve model inference, with rich data leading to high-quality fits for all methods, but DFO methods showing substantial advantages over less informative data sources better representative of the current experimental practice. |
1807.03696 | Peter Taylor | Nishant Sinha, Yujiang Wang, Justin Dauwels, Marcus Kaiser, Thomas
Thesen, Rob Forsyth, Peter Neal Taylor | Computer modelling of connectivity change suggests epileptogenesis
mechanisms in idiopathic generalised epilepsy | null | NeuroImage.Clinical 21 (2019) 101655 | 10.1016/j.nicl.2019.101655 | null | q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Patients with idiopathic generalised epilepsy (IGE) typically have normal
conventional magnetic resonance imaging (MRI), hence MRI based diagnosis is
challenging. Anatomical abnormalities underlying brain dysfunctions in IGE are
unclear and their relation to the pathomechanisms of epileptogenesis is poorly
understood. In this study, we applied connectometry, an advanced quantitative
neuroimaging technique for investigating localised changes in white-matter
tissue. Analysing white matter structures of 32 subjects we incorporated our
findings in a computational model of seizure dynamics to suggest a plausible
mechanism of epileptogenesis. Patients with IGE have significant bilateral
alterations in major white-matter fascicles. In the cingulum, fornix, and
superior longitudinal fasciculus, tract integrity is compromised, whereas in
specific parts of tracts between thalamus and the precentral gyrus, tract
integrity is enhanced in patients. Combining these alterations in a logistic
regression model, we computed the decision boundary that discriminated patients
and controls. The computational model, informed with the findings on the tract
abnormalities, specifically highlighted the importance of enhanced
cortico-reticular connections along with impaired cortico-cortical connections
in inducing pathological seizure-like dynamics. We emphasise taking
directionality of brain connectivity into consideration towards understanding
the pathological mechanisms; this is possible by combining neuroimaging and
computational modelling. Our imaging evidence of structural alterations suggest
the loss of cortico-cortical and enhancement of cortico-thalamic fibre
integrity in IGE. We further suggest that impaired connectivity from cortical
regions to the thalamic reticular nucleus offers a therapeutic target for
selectively modifying the brain circuit for reversing the mechanisms leading to
epileptogenesis.
| [
{
"created": "Tue, 10 Jul 2018 15:11:19 GMT",
"version": "v1"
},
{
"created": "Sat, 10 Nov 2018 09:31:25 GMT",
"version": "v2"
}
] | 2020-09-30 | [
[
"Sinha",
"Nishant",
""
],
[
"Wang",
"Yujiang",
""
],
[
"Dauwels",
"Justin",
""
],
[
"Kaiser",
"Marcus",
""
],
[
"Thesen",
"Thomas",
""
],
[
"Forsyth",
"Rob",
""
],
[
"Taylor",
"Peter Neal",
""
]
] | Patients with idiopathic generalised epilepsy (IGE) typically have normal conventional magnetic resonance imaging (MRI), hence MRI based diagnosis is challenging. Anatomical abnormalities underlying brain dysfunctions in IGE are unclear and their relation to the pathomechanisms of epileptogenesis is poorly understood. In this study, we applied connectometry, an advanced quantitative neuroimaging technique for investigating localised changes in white-matter tissue. Analysing white matter structures of 32 subjects we incorporated our findings in a computational model of seizure dynamics to suggest a plausible mechanism of epileptogenesis. Patients with IGE have significant bilateral alterations in major white-matter fascicles. In the cingulum, fornix, and superior longitudinal fasciculus, tract integrity is compromised, whereas in specific parts of tracts between thalamus and the precentral gyrus, tract integrity is enhanced in patients. Combining these alterations in a logistic regression model, we computed the decision boundary that discriminated patients and controls. The computational model, informed with the findings on the tract abnormalities, specifically highlighted the importance of enhanced cortico-reticular connections along with impaired cortico-cortical connections in inducing pathological seizure-like dynamics. We emphasise taking directionality of brain connectivity into consideration towards understanding the pathological mechanisms; this is possible by combining neuroimaging and computational modelling. Our imaging evidence of structural alterations suggest the loss of cortico-cortical and enhancement of cortico-thalamic fibre integrity in IGE. We further suggest that impaired connectivity from cortical regions to the thalamic reticular nucleus offers a therapeutic target for selectively modifying the brain circuit for reversing the mechanisms leading to epileptogenesis. |
1405.3021 | Thorsten Pr\"ustel | Thorsten Pr\"ustel and Martin Meier-Schellersheim | General theory of area reactivity models: rate coefficients, binding
probabilities and all that | null | null | null | null | q-bio.QM cond-mat.stat-mech | http://creativecommons.org/licenses/publicdomain/ | We further develop the general theory of the area reactivity model that
provides an alternative description of the diffusion-influenced reaction of an
isolated receptor-ligand pair in terms of a generalized Feynman-Kac equation.
We analyze both the irreversible and reversible reaction and derive the
equation of motion for the survival and separation probability. Furthermore, we
discuss the notion of a time-dependent rate coefficient within the alternative
model and obtain a number of relations between the rate coefficient, the
survival and separation probabilities and the reaction rate. Finally, we
calculate asymptotic and approximate expressions for the (irreversible) rate
coefficient, the binding probability, the average lifetime of the bound state
and discuss on- and off-rates in this context. Throughout our treatment, we
will point out similarities and differences between the area and the classical
contact reactivity model. The presented analysis and obtained results provide a
theoretical framework that will facilitate the comparison of experiment and
model predictions.
| [
{
"created": "Tue, 13 May 2014 03:12:43 GMT",
"version": "v1"
}
] | 2014-05-14 | [
[
"Prüstel",
"Thorsten",
""
],
[
"Meier-Schellersheim",
"Martin",
""
]
] | We further develop the general theory of the area reactivity model that provides an alternative description of the diffusion-influenced reaction of an isolated receptor-ligand pair in terms of a generalized Feynman-Kac equation. We analyze both the irreversible and reversible reaction and derive the equation of motion for the survival and separation probability. Furthermore, we discuss the notion of a time-dependent rate coefficient within the alternative model and obtain a number of relations between the rate coefficient, the survival and separation probabilities and the reaction rate. Finally, we calculate asymptotic and approximate expressions for the (irreversible) rate coefficient, the binding probability, the average lifetime of the bound state and discuss on- and off-rates in this context. Throughout our treatment, we will point out similarities and differences between the area and the classical contact reactivity model. The presented analysis and obtained results provide a theoretical framework that will facilitate the comparison of experiment and model predictions. |
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